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Research article

Frequency-hopping scheduling algorithm for energy-efficient IoT, long-range, wide-area networks


  • Received: 16 December 2024 Revised: 26 December 2024 Accepted: 26 December 2024 Published: 31 December 2024
  • A long-range, wide-area network is a cost-effective, energy-efficient technology for wide-area sensor networks. But the massive Internet of Things (IoT) brings challenges such as increased traffic and energy consumption. Thus, there is a pressing need to design a scheduling strategy to improve network energy efficiency without compensating for its reliability. We have proposed a deep deterministic policy gradient-based scheduling algorithm with a frequency-hopping spread spectrum that avoids repeated collisions and retransmissions. Frequency-hopping divides frequency channels into subchannels, allowing multiple devices to operate simultaneously. This makes it a favorable scheduling strategy for dense networks, as it reduces collisions and energy consumption. Scheduling in a long-range, wide-area network involves selecting transmission parameters for each device, which can be cumbersome. We used the deep deterministic policy gradient algorithm to optimize schedule generation for high-density networks, enhancing energy efficiency. In this paper, we compared the performance of the frequency-hopping spread spectrum with other heuristic and machine learning-based algorithms using the LoRaSim simulator. We observed a 42% increase in the packet delivery ratio and a 17% improvement in energy efficiency with our solution, along with detailed results on the transmission time and collision reduction.

    Citation: Jui Mhatre, Ahyoung Lee, Ramazan Aygun. Frequency-hopping scheduling algorithm for energy-efficient IoT, long-range, wide-area networks[J]. Applied Computing and Intelligence, 2024, 4(2): 300-327. doi: 10.3934/aci.2024018

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  • A long-range, wide-area network is a cost-effective, energy-efficient technology for wide-area sensor networks. But the massive Internet of Things (IoT) brings challenges such as increased traffic and energy consumption. Thus, there is a pressing need to design a scheduling strategy to improve network energy efficiency without compensating for its reliability. We have proposed a deep deterministic policy gradient-based scheduling algorithm with a frequency-hopping spread spectrum that avoids repeated collisions and retransmissions. Frequency-hopping divides frequency channels into subchannels, allowing multiple devices to operate simultaneously. This makes it a favorable scheduling strategy for dense networks, as it reduces collisions and energy consumption. Scheduling in a long-range, wide-area network involves selecting transmission parameters for each device, which can be cumbersome. We used the deep deterministic policy gradient algorithm to optimize schedule generation for high-density networks, enhancing energy efficiency. In this paper, we compared the performance of the frequency-hopping spread spectrum with other heuristic and machine learning-based algorithms using the LoRaSim simulator. We observed a 42% increase in the packet delivery ratio and a 17% improvement in energy efficiency with our solution, along with detailed results on the transmission time and collision reduction.



    Network reliability is an important topic in network research, network performance analysis, and combinatorial mathematics. And researchers usually use graph theoretic models to study it extensively. The network reliability can be separated into three types of models: edges are perfectly reliable while vertices survive independently with a fixed probability [9,12]; vertices are perfectly reliable while edges survive independently with a fixed probability [3,11,16]; and vertices and edges survive independently of each other with some fixed probabilities [6,8]. There are two aspects on the network reliability: reliability analysis and reliability design. The purpose of reliability analysis is to compute the reliability or unreliability polynomial of a given graph [7,19,23,24]. The purpose of reliability design is to find the graphs with the maximum reliability polynomial or the minimum unreliability polynomial among graphs with the same number of vertices and edges [1,2,3,8,10,11,14,16]. In addition, according to the number of vertices which are connected in the graph, these models can be divided into two main research categories: k-terminal reliability (the probability that k specified target vertices in a given graph are connected) [2,7,13,15,17,23,24]; and all-terminal reliability (the probability that the entire graph is connected) [1,3,8,9,10,11,14,16,19,21,22].

    In practice, the network is required to run normally even if some edges are fail. If each edge of the network survives independently with a fixed probability, the network with the largest connected probability of target vertices is defined as the most reliable graph. There are more results about the most reliable graphs for all-terminal graphs, seeing [1,3,10,14,21]. However, there are a few studies on reliability analysis of k-terminal networks, which calculate the reliability polynomials of graphs [7,23,24]. And there are even fewer results on the reliability design of k-terminal networks. In 2018, Betrand et al. [2] gave some important results about the most reliable two-terminal graph, and determined several locally most reliable two-terminal graphs when the vertices are perfectly reliable and the edges survive independently with probability 0p1. And they also proved that there is no uniformly most reliable two-terminal simple graph in some graph families. It is natural to consider the following problems.

    Problem. Do the three-terminal graphs have locally most reliable graph or uniformly most reliable graph as two-terminal graphs? How does one construct locally most reliable three-terminal graphs with given number of vertices and edges? Is the locally most reliable three-terminal graph also uniformly most reliable?

    With these questions, this research extends the study from the two-terminal graphs to three-terminal graphs, studies the locally most reliable three-terminal simple sparse graphs (graphs with edges less than or equal to a constant multiple of the number of vertices) and considers whether the locally most reliable graph is also the uniformly most reliable graph. The structure of this paper is organized as follows. Fundamental definitions and notations are given in Section 2. In Section 3, some locally most reliable graphs are determined for three-terminal graphs with n vertices and m edges, where n5 and 9<m4n10. Some locally most reliable graphs are further evaluated that they are not uniformly most reliable graphs, when 11<m3n5 and m2(mod3) or 3n5<m4n10. Section 4 summarizes the results of this research.

    Some basic notation is list here. For integers a,b and r, the notation ar(modb) indicates that the reminder of a divided by b is r, and ab is the largest integer not greater than ab. In this paper, we will only consider simple graphs in which there are no multiple edges and loops. In a graph G, the degree of the vertex v is the number of edges incident with v, denoted by d(v). The complete graph on n vertices is denoted by Kn, and K1,n denotes the simple graph on n+1 vertices with one vertex of degree n and n vertices of degree 1. The union of graphs G and H is the graph with vertex set V(G)V(H) and edge set E(G)E(H), which is denoted by GH. If l is a positive integer, then lG denotes the disjoint union of l copies G. The join of G and H, which is denoted by GH, has vertex set V(G)V(H) and edge set E(G)E(H){uv|uG,vH}. Suppose u and v are two vertices in G, G{uv} is the graph obtained by adding an edge between u and v to G, and G{uv} is the graph obtained by deleting the edge between u and v from graph G. For notation and terminology not defined in this paper we follow [4].

    A three-terminal graph is an undirected and simple graph G=(V(G),E(G)) with three specified target vertices r,s and t in V(G). Using Gn,m denotes the set of all three-terminal graphs on n vertices and m edges. The probability that three specified target vertices r,s,t of a graph GGn,m remain connected when each of its edges survives independently with probability p is called the three-terminal reliability (or the three-terminal reliability polynomial) of G. The rst-subgraph of G is the subgraph of G such that r,s,t are connected with each other. Let Ni(G) (simply Ni) be the number of rst-subgraphs with i edges of graph G, then the three-terminal reliability polynomial of the graph GGn,m can be defined as

    R3(G;p)=mi=2Nipi(1p)mi.

    For some three-terminal graphs with small number of vertices and edges, we can compute the reliability polynomial directly by definition. However, for a three-terminal graph with many vertices and edges, the number of rst-subgraphs with i edges is very large, which possibly leads to the loss or repetition of some rst-subgraphs in the process of finding rst-subgraphs. So, it is very difficult to determine the coefficients of the reliability polynomial by the definition. In order to obtain the reliability polynomial, it is necessary to use the Factorization approach as indicated in the following lemma.

    Lemma 2.1. ([18]) For any edge e in a three-terminal graph GGn,m, the following factorization holds:

    R3(G;p)=pR3(Ge;p)+(1p)R3(Ge;p),

    where Ge is the graph obtained by contracting the endpoints of edge e in G and Ge is the graph obtained by deleting the edge e from G.

    Example 1. Figure 1 depicts two special three-terminal graphs in G7,14 with three target vertices r,s,t. Each edge of these graphs survives independently with probability p. By definition, we have

    Figure 1.  Two special three-terminal graphs in G7,14 with three target vertices r,s,t.

    R3(G1;p)=14i=2Ni(G1)pi(1p)14i, R3(G2;p) = 14i=2Ni(G2)pi(1p)14i.

    By calculation, we get

    R3(G1;p)R3(G2;p) = 52p14490p13+2039p124898p11 + 7433p107288p9+4463p81464p7 + 63p6+122p534p4+2p3.

    Figure 2 gives a plot of R3(G1;p)R3(G2;p). Clearly, G1 is more reliable than G2 for p0 (the value of p sufficiently close to 0) and for p1 (the value of p sufficiently close to 1). In fact, by Theorems 3.3 and 3.4, it is easy to see that when n=7 and m=14, G1B7,7 and G1 is the locally most reliable graph in G7,14 for p0 and for p1. However, when p is in the range (0.1,0.65), G2 is more reliable than G1. Thus, there is no uniformly most reliable graph in G7,14.

    Figure 2.  A plot of R3(G1;p)R3(G2;p).

    According to the definitions of the locally most reliable all-terminal graph [1] and the uniformly most reliable two-terminal graph [2], we defined the locally most reliable graph and the uniformly most reliable graph for three terminal graphs.

    Definition 2.1. For p0=0 (or 1), if there is an ε>0 such that R3(G;p)R3(H;p) for all HGn,m and for all p[0,1](p0ε,p0+ε), then G is the locally most reliable graph in Gn,m for p0 (or for p1). In particular, a graph G is the uniformly most reliable graph in Gn,m, if R3(G;p)R3(H;p) for all HGn,m and all 0p1.

    In this section, we determine some locally most reliable graphs in Gn,m for 9m4n10. Then we also consider whether these locally most reliable graphs are the uniformly most reliable graphs. An rst-subgraph with i edges is minimal if it does not contain any rst-subgraphs with edges number less than i, otherwise it is non-minimal. An rst-cutset is a set of edges, whose removal makes at least two target vertices disconnected. And the rst-edge connectivity of G is the smallest size of an rst-cutset, denoted by λ(rst) or simply λ. It is difficult to find the exact cases that three target vertices are connected, which is NP-complete [20]. Therefore, we determine the most reliable graph by the following lemma.

    Lemma 3.1.([1]) Let G,HGn,m, the three-terminal reliability polynomial of G and H is

    R3(G;p)=mi=2Ni(G)pi(1p)mi and R3(H;p)=mi=2Ni(H)pi(1p)mi, respectively.

    Suppose there exist integers k and l, such that Ni(G)=Ni(H) for 2i<k or for l<im. Then

    (1) If Nk(G)>Nk(H), then R3(G;p)>R3(H;p) for p0,

    (2) If Nl(G)>Nl(H), then R3(G;p)>R3(H;p) for p1.

    From Lemma 3.1, we see that if GGn,m is the locally most reliable graph for p0, then it must contain the triangle rst and N3 is the largest among graphs containing the triangle rst in Gn,m. It is not hard to see that Ni=(mi) for mλ+1im and Nmλ=(mλ)a, where a is the number of the rst-cutsets of size λ. Then for p1, if G is the locally most reliable graph in Gn,m, then it must have the largest rst-edge connectivity λ, the number of rst-cutsets of size λ attains the minimum value.

    We first introduce some important graphs which will be used below.

    Let n4 and 0ln4 be integers. The three-terminal graph with n vertices, which is drawn as Figure 3, is denoted by An,l, where the vertex set V(An,l) is {r=v1,s=v2,t=v3,v4,,vn} and the edge set E(An,l) contains the following 3n6+l edges:

    {rs,rt,st,vivjwherei{1,2,3},4jn,v4vjwhere5jl+4.
    Figure 3.  Graph An,l.

    Let n4 and 4wn be integers. The three-terminal graph with n vertices, which is drawn as Figure 4, is denoted by Bn,w, where the vertex set V(Bn,w) is {r=v1, s=v2, t=v3, v4, , vw, , vn} and the edge set E(Bn,w) contains the following 3w7 edges:

    {rs,rt,st,vivjwherei{1,2,3},4jw1,vivwwherei{1,2}.
    Figure 4.  Graph Bn,w, where the vertex vw is associated with the target vertex set {v1,v2}.

    Theorem 3.1. Let n5 and 9m3n5 be integers and m0 or 1(mod3). Then the graph

    Am3+2,m3m3(nm32)K1

    is the locally most reliable graph in Gn,m for p0.

    Proof. Assume that n and m satisfy the given conditions and let G be the locally most reliable graph in Gn,m for p0. By Lemma 3.1, we see that G must contain the triangle rst and N3 is the largest among graphs containing the triangle rst in Gn,m.

    According to the rst-subgraph containing the edge number of triangle rst, the rst-subgraph with 3 edges are consisted of the following four cases:

    Case 1. All of three edges are from the triangle rst, saying rs,rt,st.

    Case 2. Two of three edges are from the triangle rst and another edge not in the triangle, saying rs,st,vivj, where 1in,4jin.

    Case 3. One of three edges is from the triangle rst and other two edges are not in the triangle, saying rvi,vis,rt, where 4in.

    Case 4. None of three edges is from the triangle rst, then they are vir, vis, vit, where 4in.

    Note that the number of the rst-subgraphs in Cases 1 and 2 is 1 and 3(m3), respectively. N3 attains the maximum value if and only if the number of the rst-subgraphs of both Case 3 and Case 4 attains maximum value. By calculation, if the number of the rst-subgraphs in Case 4 attains maximum value, then E(G) must contain m33=m31 edge sets {vir,vis,vit} (4im3+2). Since m0 or 1(mod3), the number of the rst-subgraphs in Case 3 attains the maximum value, while it is maximum in Case 4. Therefore we get the following.

    If m0(mod3), then E(G)={rs,rt,st}{vivj|1i3,4jm3+2}, and G is Am3+2,0 (nm32)K1.

    If m1(mod3), the edge set of G is consisted of the triangle rst, m43 edge subsets as {vir,vis,vit} (4im+53), and the remaining edge either joining one target vertex and one non-target vertex or connecting two non-target vertices. For convenience, the remaining edge is denoted by e. If 9m3n11, then the degrees of non-target vertices in Ge are 3 or 0. There are four possible joining types for e. The first type is to connect two non-target vertices of degree 3, without losing generality setting e=v4v5, and the final graph is denoted by G1. The second type is to join one non-target vertex of degree 3 and the other non-target vertex of degree 0, without losing generality setting e=v4vm+83, and the final graph is denoted by G2. The third type is to connect two non-target vertices of degree 0, without losing generality setting e=vm+83vm+113, and the final graph is denoted by G3. The fourth type is to join one target vertex and one non-target vertex of degree 0, without losing generality setting e=rv(m+8)/3, and the final graph is denoted by G4. By calculation, N4(G1)>N4(Gi) for i{2,3,4}. By Lemma 3.1, E(G)=E(G1)={rs,rt,st,v4v5}{vivj|1i3,4jm+53}, which implies that GAm+53,1(nm+53)K1. If 3n11<m3n8, then there are n4 non-target vertices of degree 3 and only one non-target vertex of degree 0 in Ge. There are three possible types for e and the final graph is in {G1,G2,G4}. By calculation and comparison, we can see that GAm+53,1(nm+53)K1. If 3n8<m3n5, then the degrees of all non-target vertices in Ge are equal to 3. Then e is only one type to connect two non-target vertices of degree 3, which means that GG1. So, GAm+53,1(nm+53)K1.

    From the above argument, we see that Am3+2,m3m3(nm32)K1 is the locally most reliable graph in Gn,m for p0.

    Theorem 3.2. Let n5 and 9m3n5 be integers and m0 or 1(mod3). Then the graph

    Am3+2,m3m3(nm32)K1

    is the locally most reliable graph in Gn,m for p1.

    Proof. Let A=Am3+2,m3m3(nm32)K1. Let GGn,m be the locally most reliable graph for p1. Then by Lemma 3.1, G must have the largest rst-edge connectivity λ, and the number of rst-cutsets of size λ attains the minimum value among graphs with the largest λ.

    Obviously, λmin{d(r),d(s),d(t)}m3+1. If {rs,rt,st}{vivj|1i3,4jm3+2}E(G), then min{d(r),d(s),d(t)}=m3+1. Let C be the minimal rst-cutset of G, then there must exist a component containing just one target vertex and k1 (0k1n3) non-target vertices ui(1ik1) in GC, where u1,u2,,uk{vi4im3+2}, and without loss of generality, setting this target vertex as r. Then the number of edges in C is at least m3+1k+2k=m3+1+km3+1. Thus λm3+1. Hence, λ can arrive at the maximum value m3+1 if {rs,rt,st}{vivj|1i3,4jm3+2}E(G). Then λ=m3+1.

    If m0(mod3), then λ is m3+1, d(r)=d(s)=d(t)=m3+1, r,s and t are adjacent with each other. If there is a non-target vertex vV(G) with d(v)0 or 3, then we have either λ(G)<m3+1, or Nm(m3+1)(G)<(mλ)3. For each non-target vertex vV(G), if d(v)=0 or 3, then Nmλ(G)=(mλ)3. Since G is the locally most reliable graph, by Lemma 3.1, Nmλ must be maximum, then the degree of each non-target vertex is either 0 or 3. Thus, G is A.

    If m1(mod3), then λ is m+23, and {rs,rt,st}E(G)2. When {rs,rt,st}E(G)=3, similarly, we can find that there are four graphs with λ=m+23 and Nmλ=(mλ)3, which are A, A{rvn}{v4v5}, A{v4vn}{v4v5}, A{vn1vn}{v4v5}, where the second and third graphs only occur when m3n8 and the last only occurs when m3n11. By calculation, the values of Nmλ1 of these four graphs is (mλ+1)3m+12, (mλ+1)3m+6, (mλ+1)3m+6 and (mλ+1)3m+6, respectively. Obviously, (mλ+1)3m+12>(mλ+1)3m+6, by Lemma 3.1, G is A. When {rs,rt,st}E(G)=2, similarly, by calculation, we find that for all graphs with λ=m+23, there is Nmλ<(mλ)3. Therefore, by Lemma 3.1, if m1(mod3), then {rs,rt,st}E(G)=3 and G is A.

    Therefore, the graph Am3+2,m3m3(nm32)K1 is the locally most reliable graph in Gn,m for p1.

    Theorems 3.1 and 3.2 show that when n5, 9m3n5 and m0 or 1(mod3), Am3+2,m3m3 (nm32)K1 is the locally most reliable graph in Gn,m for both p0 and p1. If m2(mod3), we have the following theorems, whose proofs are similar to the proofs of Theorems 3.1 and 3.2.

    Theorem 3.3. Let n5 and 9m3n5 be integers and m2(mod3). Then the graph

    Bn,m3+3(nm33)K1

    is the locally most reliable graph in Gn,m for p0.

    Theorem 3.4. Let n5 and 9m3n5 be integers and m2(mod3). Then the graph

    Bn,m3+3(nm33)K1

    is the locally most reliable graph in Gn,m for p1.

    Theorems 3.3 and 3.4 show that when n5, 9m3n5 and m2(mod3), Bn,m3+3 (nm33)K1 is the locally most reliable graph in Gn,m for both p0 and p1. Is it the uniformly most reliable graph for 11<m3n5 (n5)? In order to solve this problem, we need to compute the reliability polynomials of some three-terminal graphs.

    Lemma 3.2. Let n4 be an integer. Then

    R3(An,0;p)=1(4p618p5+30p420p3+6p2)(13p2+2p3)n43(1p)2(12p2+p3)n3.

    Proof. The vertices in An,0 are labeled same as Figure 3. By Lemma 2.1, we can calculate a recurrence relation for the three-terminal probability polynomial of An,0.

    R3(An,0;p)=p3R3(G1;p)+p3(1p)R3(G2;p) + p2(1p)2R3(G3;p) + p3(1p)R3(G4;p)+p2(1p)2R3(G5;p) + p(1p)2R3(G6;p)+p3(1p)R3(G7;p)+p2(1p)2R3(G8;p) + p(1p)2R3(G9;p)+p(1p)2R3(G10;p) + (1p)3R3(G11;p), where the forms and reliability polynomials of Gi (1i11) are shown in Table 1.

    Table 1.  Reliability polynomials of graphs for R3(An,0;p).
    Graph Gi Reliability polynomial of Gi
    G1=An,0v1vnv1v2v1v3 1
    G2=An,0v1vnv1v2v1v3v2v3 1
    G3=An,0v1vnv1v2v1v3v2v3 1(1p)(12p2+p3)n4
    G4=An,0v1vnv1v2v1v3v2v3 1
    G5=An,0v1vnv1v2v1v3v2v3 1(1p)(12p2+p3)n4
    G6=An,0v1vnv1v2v1v3 R3(An1,0;p)
    G7=An,0v1vnv2vnv2v3v1v2 1
    G8=An,0v1vnv2vnv2v3v1v2 1(1p)(12p2+p3)n4
    G9=An,0v1vnv2vnv2v3 R3(An1,0;p)
    G10=An,0v1vnv2vnv3vn R3(An1,0;p)
    G11=An,0v1vnv2vnv3vn R3(An1,0;p)

     | Show Table
    DownLoad: CSV

    By Table 1, we have R3(An,0;p)=(1+2p)(1p)2R3(An1,0;p)+p3(43p)+3p2(1p)2[1(1p)(12p2+p3)n4].

    Calculating the linear non-homogeneous recurrence relation, we have

    R3(An,0;p)=(1+2p)n4(1p)2n8R3(A4,0;p)+1(1+2p)n4(1p)2n81(1+2p)(1p)2(3p22p3)3p2(1p)31((1+2p)(1p)2/12p2+p3)n41[(1+2p)(1p)2/12p2+p3]=(4p6+15p518p4+5p3+3p2)(13p2+2p3)n4+[1(13p2+2p3)n4]3(1p)2(12p2+p3)n3[1(1(3p2+2p3)/(12p2+p3))n4]=1(4p618p5+30p420p3+6p2)(13p2+2p3)n43(1p)2(12p2+p3)n3.

    The proof is completed.

    Similarly as Lemma 3.2, we can get Lemmas 3.3 and 3.4.

    Lemma 3.3. Let n6 and 4wn be integers. Then

    R3(Bn,w;p)=p3+p2(1p)[1(1p)(12p2+p3)w4]+(p+1)(1p)Aw1,0.

    Lemma 3.4. Let n6 be an integer. Then

    R3(An,2;p)=3p1024p9+80p8138p7+120p630p528p4+18p3 - (18p12159p11+603p101272p9+1602p81173p7+399p6 + 42p578p4+18p3)(12p2+p3)n6(3p1024p9+81p8150p7 + 165p6108p5+39p46p3)An3,0+(p812p7 + 45p680p5+75p436p3+7p2)An2,0 + (2p58p4+12p38p2+2p)An1,0+(p22p+1)An,0.

    With the above lemmas, we can get Theorem 3.5.

    Theorem 3.5. Let n5 and 11<m3n5 be integers and m2(mod3). Then the graph Am3+2,2(nm32)K1 is more reliable than Bn,m3+3(nm33)K1 in Gn,m for p=1/2.

    Proof. For the convenience, let w=m3+3. By Lemma 3.2, we have

    R3(An,0;1/2)=1+(1/2)n1(3/4)(5/8)n3.

    By Lemmas 3.3 and 3.4 and R3(An,0;1/2), we have

    R3(Bn,w;1/2)=14116(5/8)w4+34R3(Aw1,0;1/2)=1(5/8)w3+34(1/2)w2,
    R3(Aw1,2;1/2)=6431024364(5/8)w7+91024R3(Aw4,0;1/2)+3256R3(Aw3,0;1/2)+116R3(Aw2,0;1/2)+14R3(Aw1,0;1/2)=15794096(5/8)w7+831024(1/2)w5.

    Thus, R3(Aw1,2;1/2)R3(Bn,w;1/2)=14096[46(5/8)w713(1/2)w7].

    Since 46(5/8)w7>46(1/2)w7>13(1/2)w7 (w=m3+37),

    R3(Aw1,2;1/2)R3(Bn,w;1/2)>0, which means, R3(Aw1,2;1/2)>R3(Bn,w;1/2).

    The proof is completed.

    As a straightforward consequence of Theorems 3.3 or 3.4 and 3.5, we obtain the following result.

    Theorem 3.6. Let n and m be integers. If n5, 11<m3n5 and m2(mod3), then there is no uniformly most reliable graph in Gn,m.

    Now, the existence of uniformly most reliable graph with edges less than 3n5 is solved partly. How about the same question for a little more edges?

    Lemma 3.5.([5]) Let n1 and 0mn1 be integers.

    If m3, then the unique simple graph on n vertices and m edges with the maximum number of paths of length 2 is K1,m(nm1)K1.

    If m=3, there are two simple graphs with the maximum number of paths of length 2: K3(n3)K1 and K1,3(n4)K1.

    Theorem 3.7. Let n7 and 3n5<m4n10 be integers. Then the graph An,m3n+6 is the locally most reliable graph in Gn,m for p0.

    Proof. Let GGn,m be the locally most reliable graph for p0. By Lemma 3.1 and the proof of Theorem 3.1, it is easy to see that G must contain the triangle rst and n3 edge sets {rvi,svi,tvi} (4in). Thus, we need to determine the remaining l=m3n+6 edges between non-target vertices. For convenience, using ˆG denotes the subgraph of G induced by all the non-target vertices, then E(ˆG)=l. Since the different structures of ˆG may lead different Ni when i4, we begin with N4, which is the number of rst-subgraphs with 4 edges.

    The rst-subgraphs with 4 edges of G can be divided into two cases. Some of them is minimal and others is non-minimal. There are three forms of the minimal rst-subgraph with 4 edges, which are {svi,vit,svj,vjr}, {svi,vivj,vjt,sr}, and {svi,vivj,vjt,vjr} (4i,jn,ij). The number of these three edge sets is 6(n32), 12l and 6l, respectively. We can see that the number of the minimal rst-subgraphs with 4 edges is affected by l, regardless of the structure of ˆG.

    The non-minimal rst-subgraph with 4 edges of G include the following cases:

    C1: the minimal rst-subgraph with 2 edges,

    C2: the minimal rst-subgraph with 3 edges but no minimal rst-subgraph with 2 edges.

    By calculation, the number of the non-minimal rst-subgraphs with 4 edges in C1 and C2 is 3(m32)+(m3) and (n3)(m3)+6(n3)(m6), respectively. Then the number of the non-minimal rst-subgraphs with 4 edges is a constant for given n and m.

    Therefore, whatever the structure of ˆG is, N4 is a constant for given n and m. Then we need to consider N5, which is the number of rst-subgraphs with 5 edges.

    The rst-subgraphs with 5 edges of G can be divided into two cases. Some of them is minimal and others is non-minimal. The non-minimal rst-subgraph with 5 edges of G include the following cases:

    D1: the minimal rst-subgraph with 2 edges,

    D2: the minimal rst-subgraph with 3 edges but no minimal rst-subgraph with 2 edges,

    D3: the minimal rst-subgraph with 4 edges but no minimal rst-subgraph with less than 4 edges.

    By calculation, the number of the non-minimal rst-subgraphs with 5 edges in D1, D2 and D3 is 3(m33)+(m32), 7(n3)(m62)+3(n3)(m6)12(n32) and 18l(m10)+6l+6(m9)(n32), respectively. Then the number of the non-minimal rst-subgraphs with 5 edges is a constant for given n and m.

    There are four forms of the minimal rst-subgraph with 5 edges, which are {svi,vivj,vjr,rvk, vkt}, {svi,vivj,vjvk,vkt,rt}, {svi,vivj,vjr,vjvk,vkt}, and {rvi,svi,vivj,vjvk,vkt} (4i, j,kn,ijk). By calculation, the number of the first edge set is 12l(n5), which is affected by l, regardless of the structure of ˆG. But the number of other three cases affected by the number of P3 in ˆG. By Lemma 3.5, if l3 and ln4, then the number of P3 in ˆG is maximum if ˆG is K1,l(nl4)K1, and G is K3(K1,l(nl4)K1). If l=3, the number of P3 in ˆG is maximum only if ˆG is either K3(n6)K1 or K1,3(n7)K1, and G is either K3(K3(n6)K1) or K3(K1,3(n7)K1). Then by Lemma 3.1, we need to compare N6(K3(K3(n6)K1)) and N6(K3(K1,3(n7)K1)). According to the calculation method of N4 and N5, we can get that the difference of coefficient N6s of the front two graphs is 72. Then for p0, G is K3(K1,3(n7)K1).

    From the above argument, we conclude that the graph An,m3n+6 is the locally most reliable graph in Gn,m for p0.

    Now, two classes of graphs are given, which will be used in the following theorems.

    Let n4 and 0ln32 be integers. The three-terminal graph with n vertices, which is drawn as Figure 5, is denoted by Cn,l, where the vertex set V(Cn,l) is {r=v1,s=v2,t=v3,v4,,vn} and the edge set E(Cn,l) contains the following 3n9+l edges:

    {rs,rt,st,vivjwherei{1,2,3},4jn,v2iv2i+1where2il+1.
    Figure 5.  Graph Cn,l (left) and Graph Cn,l (right).

    Let n4 and 0ln4 be integers. The three-terminal graph with n vertices, which is drawn as Figure 5, is denoted by Cn,l, where the vertex set V(Cn,l) is {r=v1,s=v2,t=v3,v4,,vn} and the edge set E(Cn,l) contains the following 3n9+l edges:

    {rs,rt,st,vivjwherei{1,2,3},4jn,vjvj+1where4jl+3.

    Theorem 3.8. Let n7 and 3n5<m3n6+n32 be integers. Then the graph Cn,m3n+6 is the unique locally most reliable graph in Gn,m for p1.

    Proof. Let GGn,m be the unique locally most reliable graph for p1. Then by Lemma 3.1, the value of the rst-edge connectivity λ of G must be as large as possible.

    let C be the minimal rst-cutset of G, then there must exist a component containing just one target vertex and k (0kn3) non-target vertices ui (1ik) in GC, without loss of generality, setting this target vertex as r. Clearly, λmin{d(r),d(s),d(t)}n1. If d(r)=d(s)=d(t)=n1, then |C|d(r)k+2k=d(r)+kn1. Hence, λ can arrive at the maximum value n1 if and only if d(r)=d(s)=d(t)=n1. Then, G contains the triangle rst and n3 edge sets as {rvi,svi,tvi} (4in). These 3n6 edges are confirmed, we also need to determine the remaining m3n+6 edges connecting non-target vertices.

    By Lemma 3.1, we need to compare the number of rst-subgraph with mn+1 edges, which is denoted as Nmn+1, of graphs with λ=n1. Continue to calculate the minimal rst-cutset of G, |C|=d(r)k+ki=1[d(ui)1]2m=n2k2m1+ki=1d(ui), where m is the number of edges between these k non-target vertices. It is clear to see that ki=1d(ui)3k+2m, thus |C|n+k1. Then, we can get that the component of k+1 vertices generated by deleting the minimal rst-cutset of size n1 contains only the target vertex. Thus, we have Nmn+1=(mn1)3, which is a constant for given n and m. By Lemma 3.1, we need to consider Nmn.

    The component of k+1 vertices generated by deleting the minimal rst-cutset of size n contains one target vertex and one non-target vertex of degree 3. Thus, we have Nmn=(mn)3(mn+11)3(a1), where a is the number of non-target vertices with degree 3. Since Cn,m3n+6 has the fewest non-target vertices with degree 3 in graphs with λ=n1, Nmn(Cn,m3n+6) gets the maximum value.

    Therefore, Cn,m3n+6 is the locally most reliable graph in Gn,m for p1.

    Theorem 3.9. Let n7 and 3n6+n32<m4n10 be integers. Then the graph Cn,m3n+6 is more reliable than An,m3n+6 in Gn,m for p1.

    Proof. For convenience, let l=m3n+6. In An,l, there are n4l vertices of degree 3, l vertices of degree 4, a vertex of degree l+3 and 3 target vertices of degree n1. And Cn,l has n4l vertices of degree 3, 2 vertices of degree 4, l1 vertices of degree 5 and 3 target vertices of degree n1. The rst-edge connectivity λ of An,l and Cn,l are the same, where λ=n1.

    It is easy to calculate that

    Nmj(An,l)=Nmj(Cn,l)=(mj) (0jn2);

    Nmλ(An,l)=Nmλ(Cn,l)=(mλ)3;

    Nmλ1(An,l)=Nmn(An,l)=Nmλ1(Cn,l)=(mn)3(ml3);

    and

    Nmλ2(An,l)=Nmn1(An,l)=(mn+1)3(n4l2)3(mn+12)3(n4l)(mn1)3l;
    Nmλ2(Cn,l)=Nmn1(Cn,l)=(mn+1)3(n4l2)3(mn+12)3(n4l)(mn1)6.

    Since l=m3n+63, Nmλ2(Cn,l)>Nmλ2(An,l).

    By the Lemma 3.1, Cn,m3n+6 is more reliable than An,m3n+6 in Gn,m for p1.

    We give the locally most reliable graph in Gn,m with 3n5<m3n6+n32 (n7) for p1, as shown in Theorem 3.8. If 3n6+n32<m4n10 (n7), we construct a graph with m edges that is more reliable than An,m3n+6 for p1, as shown in Theorem 3.9. Thus, we obtain the following result.

    Theorem 3.10. Let n and m be integers. If n7 and 3n5<m4n10, then there is no uniformly most reliable graph in Gn,m.

    This research focuses on characterizing the locally most reliable graph for three-terminal spare graphs. There is rare literature on the locally most reliable graph for three-terminal graphs. Based on the results of this research, the following conclusions can be drawn.

    If 9m3n5 (n5) and m2(mod3), the locally most reliable graph for p0 and p1 are determined with theoretical proofs. It is also proved that there is no uniformly most reliable three-terminal graph when 11<m3n5 (n5) and m2(mod3).

    The locally most reliable graph in Gn,m for p0 is determined with proofs when 3n5<m4n10 (n7). The locally most reliable graph in Gn,m for p1 for 3n5<m3n6+n32 (n7) is also determined with proofs. Additionally, it is proved that there is no uniformly most reliable three-terminal graph when 3n5<m4n10 (n7).

    If 9m3n5 (n5) and m0 or 1(mod3), as shown in Theorems 3.1 and 3.2, the locally most reliable graphs for p0 is also locally most reliable for p1. However, it is still unknown whether the locally most reliable graph is the uniformly most reliable graph for 9m3n5 (n5) and m0 or 1(mod3) for all 0p1.

    The results of the research can be useful for designing highly reliable networks which have three target vertices. The findings of this research provide guiding significance for determining the locally most reliable graphs for general k-terminal networks.

    Research supported by the National Science Foundation of China (Grant Nos. 11801296), the Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province, the Science Found of Qinghai Province (Grant No. 2018-ZJ-718), the Key Laboratory of Tibetan Information Processing Ministry of Education, and Tibetan Information Processing Engineering Technology and Research Center of Qinghai Province.

    The authors declare no conflict of interest in this paper.



    [1] M. Torchia, A. Shrivastava, K. Chinta, E. Elshewy, M. Fang, N. Guo, et al., Worldwide internet of things spending guide, IDC Corporate, 2019. Available from: https://www.idc.com/getdoc.jsp?containerId=IDC_P29475.
    [2] Semtech, What is LoRa, Semtech Corporation, 2024. Available from: https://www.semtech.com/lora/what-is-lora.
    [3] G. Boquet, P. Tuset-Peiró, F. Adelantado, T. Watteyne, X. Vilajosana, Lr-fhss: overview and performance analysis, IEEE Commun. Mag., 59 (2021), 30–36. https://doi.org/10.1109/MCOM.001.2000627 doi: 10.1109/MCOM.001.2000627
    [4] Y. Li, J. Yang, J. Wang, Dylora: towards energy efficient dynamic lora transmission control, Proceedings of IEEE INFOCOM 2020—IEEE Conference on Computer Communications, 2020, 2312–2320. https://doi.org/10.1109/INFOCOM41043.2020.9155407
    [5] L. Tu, A. Bradai, Y. Pousset, A. Aravanis, Energy efficiency analysis of LoRa networks, IEEE Wirel. Commun. Le., 10 (2021), 1881–1885. https://doi.org/10.1109/LWC.2021.3084996 doi: 10.1109/LWC.2021.3084996
    [6] SonicWall, Wireless: SNR, RSSI and noise basics of wireless troubleshooting, SonicWall, 2019. Available from: https://www.sonicwall.com/support/knowledge-base/wireless-snr-rssi-and-noise-basics-of-wireless-troubleshooting/180314090744170/.
    [7] R. Sanchez-Iborra, J. Sánchez-Gómez, J. Santa, P. J. Fernández, A. F. Skarmeta, IPv6 communications over LoRa for future IoV services, Proceedings of IEEE 4th World Forum on Internet of Things (WF-IoT), 2018, 92–97. https://doi.org/10.1109/WF-IoT.2018.8355231
    [8] A. Gloria, C. Dionisio, G. Simões, P. Sebastião, Lora transmission power self configuration for low power end devices, Proceedings of 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC), 2019, 1–6. https://doi.org/10.1109/WPMC48795.2019.9096197
    [9] D. Zorbas, K. Q. Abdelfadeel, V. Cionca, D. Pesch, B. O'Flynn, Offline scheduling algorithms for time-slotted lora-based bulk data transmission, Proceedings of IEEE 5th World Forum on Internet of Things (WF-IoT), 2019,949–954. https://doi.org/10.1109/WF-IoT.2019.8767277
    [10] D. Zorbas, C. Caillouet, K. Abdelfadeel Hassan, D. Pesch, Optimal data collection time in lora networks—a time-slotted approach, Sensors, 21 (2021), 1193. https://doi.org/10.3390/s21041193 doi: 10.3390/s21041193
    [11] C. Pham, A. Bounceur, L. Clavier, U. Noreen, M. Ehsan, Radio channel access challenges in LoRa low-power wide-area networks, LPWAN Technologies for IoT and M2M Applications, 2020, 65–102. https://doi.org/10.1016/B978-0-12-818880-4.00004-1
    [12] J. Moraes, N. Matni, A. Riker, H. Oliveira, E. Cerqueira, C. Both, et al., An efficient heuristic LoRaWAN adaptive resource allocation for IoT applications, Proceedings of IEEE Symposium on Computers and Communications (ISCC), 2020, 1–6. https://doi.org/10.1109/ISCC50000.2020.9219600
    [13] K. Q. Abdelfadeel, D. Zorbas, V. Cionca, D. Pesch, Free—fine-grained scheduling for reliable and energy-efficient data collection in lorawan, IEEE Internet Things, 7 (2020), 669–683. https://doi.org/10.1109/JIOT.2019.2949918 doi: 10.1109/JIOT.2019.2949918
    [14] J. Mhatre, A. Lee, H. Lee, Frequency hopping scheduling algorithm in green lorawan: reinforcement learning approach, Proceedings of IEEE Conference on Standards for Communications and Networking (CSCN), 2023,216–221. https://doi.org/10.1109/CSCN60443.2023.10453154
    [15] T. Yatagan, S. Oktug, Smart spreading factor assignment for lorawans, Proceedings of IEEE Symposium on Computers and Communications (ISCC), 2019, 1–7. https://doi.org/10.1109/ISCC47284.2019.8969608
    [16] M. El-Aasser, A. Gasser, M. Ashour, T. Elshabrawy, Performance analysis comparison between LoRa and frequency hopping-based LPWAN, Proceedings of IEEE Global Conference on Internet of Things (GCIoT), 2019, 1–6. https://doi.org/10.1109/GCIoT47977.2019.9058411
    [17] A. Varga, R. Hornig, An overview of the OMNeT++ simulation environment, Proceedings of 1st International ICST Conference on Simulation Tools and Techniques for Communications, Networks and Systems, 2010, 1–10. https://doi.org/10.4108/ICST.SIMUTOOLS2008.3027
    [18] M. A. Ullah, K. Mikhaylov, H. Alves, Analysis and simulation of LoRaWAN LR-FHSS for direct-to-satellite scenario, IEEE Wirel. Commun. Le., 11 (2022), 548–552. https://doi.org/10.1109/LWC.2021.3135984 doi: 10.1109/LWC.2021.3135984
    [19] M. C. Bor, U. Roedig, T. Voigt, J. M. Alonso, Do LoRa low-power wide-area networks scale? Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, 2016, 59–67. https://doi.org/10.1145/2988287.2989163
    [20] I. Ilahi, M. Usama, M. O. Farooq, M. U. Janjua, J. Qadir, Intelligent resource allocation in dense lora networks using deep reinforcement learning, arXiv: 2012.11867. https://doi.org/10.48550/arXiv.2012.11867
    [21] R. Hamdi, E. Baccour, A. Erbad, M. Qaraqe, M. Hamdi, LoRa-RL: deep reinforcement learning for resource management in hybrid energy lora wireless networks, IEEE Internet Things, 9 (2022), 6458–6476. https://doi.org/10.1109/JIOT.2021.3110996 doi: 10.1109/JIOT.2021.3110996
    [22] J. Mhatre, A. Lee, Dynamic reinforcement learning based scheduling for energy-efficient edge-enabled lorawan, Proceedings of IEEE International Performance, Computing, and Communications Conference (IPCCC), 2022,412–413. https://doi.org/10.1109/IPCCC55026.2022.9894340
    [23] LoRaWAN, Notice of use and disclosure, LoRa Alliance, Inc., 2020. Available from: https://lora-alliance.org/wp-content/uploads/2020/11/RP_2-1.0.2.pdf.
    [24] L. Zhang, B. Yang, X. You, Received signal strength indicator-based recursive set-membership localization with unknown transmit power and path loss exponent, IEEE Sens. J., 21 (2021), 26175–26185. https://doi.org/10.1109/JSEN.2021.3118536 doi: 10.1109/JSEN.2021.3118536
    [25] J. Miranda, R. Abrishambaf, T. Gomes, P. Gonçalves, J. Cabral, A. Tavares, et al., Path loss exponent analysis in wireless sensor networks: Experimental evaluation, Proceedings of 11th IEEE international conference on industrial informatics (INDIN), 2013, 54–58. https://doi.org/10.1109/INDIN.2013.6622857
    [26] D. Hermans, When it comes to Wi-Fi coverage, green is not always a good color, Cambium Networks, 2018. Available from: https://www.cambiumnetworks.com/blog/when-it-comes-to-wi-fi-coverage-green-is-not-always-a-good-color/.
    [27] D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, M. Riedmiller, Deterministic policy gradient algorithms, Proceedings of the 31st International Conference on Machine Learning, 2014,387–395.
    [28] Y. Wang, W. Fang, Y. Ding, N. Xiong, Computation offloading optimization for UAV-assisted mobile edge computing: a deep deterministic policy gradient approach, Wireless Netw., 27 (2021), 2991–3006. https://doi.org/10.1007/s11276-021-02632-z doi: 10.1007/s11276-021-02632-z
    [29] RF Wireless, LoRaWAN MAC layer message formats, RF Wireless World, 2024. Available from: https://www.rfwireless-world.com/Tutorials/LoRaWAN-MAC-layer-inside.html.
    [30] K. Koriakin, R. K. Boughton, Vaginal birthing sensors as a tool to monitor calving on large scale applications, Comput. Electron. Agr., 182 (2021), 106035. https://doi.org/10.1016/j.compag.2021.106035 doi: 10.1016/j.compag.2021.106035
    [31] The Things Network, RSSI and SNR, The Things Industries, 2024. Available from: https://www.thethingsnetwork.org/docs/lorawan/rssi-and-snr/.
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