A Fast Neighbor Discovery Algorithm in WSNs

Wei, Liangxiong; Sun, Weijie; Chen, Haixiang; Ping, Yuan; Yin, Feng; Luo, Qian; Chen, Yanru; Chen, Liangyin

doi:10.3390/s18103319

Cited by 20 publications

(20 citation statements)

References 33 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Euclidean Distance between two nodes n i , and n j is denoted by d i,j . A node n i ∈ N has coordinates (x i , y i ), and is aware of the position of its neighbors using any Neighbor Discovery strategy such as the one in [26]. The set of neighbors for a node n i ∈ N is the subset N i ⊆ N such that N i = {n j |n j ∈ N d i,j ≤ δ}, and m i is the number of neighboring nodes for n i .…”

Section: Assumptionsmentioning

confidence: 99%

“…To begin with, the probability of a one-hop connection between the source node n s and the destination node n j is considered only when n j is a neighboring node of n s (i.e d s,j ≤ δ). Hence, the probability density function for the shortest Euclidean distance d s,j as defined in [32] is illustrated in (26), where D is the network density. Then using (26) the probability of a one hop connection is derived as in (27).…”

Section: Expected Number Of Hopsmentioning

confidence: 99%

See 1 more Smart Citation

Tuft: Tree Based Heuristic Data Dissemination for Mobile Sink Wireless Sensor Networks

Busaileh

Hawbani

Wang

et al. 2022

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) with a static sink suffer from concentrated data traffic in the vicinity of the sink, which increases the burden on the nodes surrounding the sink, and impels them to deplete their batteries faster than other nodes in the network. Mobile sinks solve this corollary by providing a more balanced traffic dispersion, by shifting the traffic concentration with the mobility of the sink. However, it brings about a new expenditure to the network, where prior to delivering data, nodes are obligated to procure the sink's current position. This paper proposes Tuft, a novel hierarchical tree structure that is able to avert the overhead cost from delivering the fresh sink's position while maintaining a uniform dispersion of data traffic concentration. Tuft appropriates the mobility of the sink to its advantage, to increase the uniformity of energy consumption throughout the network. Moreover, we propose Tuft-Cells, a distributed dissemination protocol that models data routing as a Multi-Criteria Decision Making (MCDM) in three steps. To begin with, each criterion constitutes a random variable defined by a mass function. Each of these cirterion serves a proportionately distinguishable alternative, and hence, may conflict. Therefore, the Analytic Hierarchy Process (AHP) quantifies the relationship between criteria. Finally, the final forwarding decision is derived by a weighted aggregation. Tuft is compared with state-of-the-art protocols, and the performance evaluation illustrates that our protocol adheres to the requirements of WSNs, in terms of energy consumption, and success ratio, considering the additional overhead cost brought by the mobility of the sink.

show abstract

Section: Assumptionsmentioning

confidence: 99%

Section: Expected Number Of Hopsmentioning

confidence: 99%

Tuft: Tree Based Heuristic Data Dissemination for Mobile Sink Wireless Sensor Networks

Busaileh

Hawbani

Wang

et al. 2022

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

show abstract

“…The bridging process is initiated by the central station (CS). Drones are sent to discover the isolated islands using discovery algorithms such as the Fast Neighbor Discovery Algorithm (FNDA) [28]. Drones calculate the boundary set for each neighborhood N i using alpha-shape algorithm.…”

Section: A(mentioning

confidence: 99%

MCCM: An Approach for Connectivity and Coverage Maximization

Ghaddar¹,

Hatoum²,

Fadlallah³

et al. 2020

Future Internet

View full text Add to dashboard Cite

The internet of Things (IoT) has attracted significant attention in many applications in both academic and industrial areas. In IoT, each object can have the capabilities of sensing, identifying, networking and processing to communicate with ubiquitous objects and services. Often this paradigm (IoT) using Wireless Sensor Networks must cover large area of interest (AoI) with huge number of devices. As these devices might be battery powered and randomly deployed, their long-term availability and connectivity for area coverage is very important, in particular in harsh environments. Moreover, a poor distribution of devices may lead to coverage holes and degradation to the quality of service. In this paper, we propose an approach for self-organization and coverage maximization. We present a distributed algorithm for “Maintaining Connectivity and Coverage Maximization” called M C C M . The algorithm operates on different movable devices in homogeneous and heterogeneous distribution. It does not require high computational complexity. The main goal is to keep the movement of devices as minimal as possible to save energy. Another goal is to reduce the overlapping areas covered by different devices to increase the coverage while maintaining connectivity. Simulation results show that the proposed algorithm can achieve higher coverage and lower nodes’ movement over existing algorithms in the state of the art.

show abstract

“…In recent years, wireless sensor networks have been extensively studied [1,[3][4][5][6][7][8][23][24][25][26][27][28]; as a cornerstone of constructing wireless sensor networks, increasingly sophisticated protocols for neighbor discovery have been proposed. These neighbor-discovery protocols mainly fall into two categories.…”

Section: Related Workmentioning

confidence: 99%

A Low-Latency and Energy-Efficient Neighbor Discovery Algorithm for Wireless Sensor Networks

Cao

Tian

et al. 2020

Sensors

View full text Add to dashboard Cite

Wireless sensor networks have been widely adopted, and neighbor discovery is an essential step to construct the networks. Most existing studies on neighbor discovery are designed on the assumption that either all nodes are fully connected or only two nodes compose the network. However, networks are partially connected in reality: some nodes are within radio range of each other, while others are not. Low latency and energy efficiency are two common goals, which become even more challenging to achieve at the same time in partially connected networks. We find that the collision caused by simultaneous transmissions is the main obstruction of achieving the two goals. In this paper, we present an efficient algorithm called Panacea to address these challenges by alleviating collisions. To begin with, we design Panacea-NCD (Panacea no collision detection) for nodes that do not have a collision detection mechanism. When n is large, we show the discovery latency is bounded by O ( n · ln n ) for any duty cycle (the percentage time to turn on the radio), where each node has n neighbors on average. For nodes that can detect collisions, we then present Panacea-WCD which also bounds the latency within O ( n · ln n ) slots. Finally, we conduct extensive evaluations and the results also corroborate our analyses.

show abstract

A Fast Neighbor Discovery Algorithm in WSNs

Cited by 20 publications

References 33 publications

Tuft: Tree Based Heuristic Data Dissemination for Mobile Sink Wireless Sensor Networks

Tuft: Tree Based Heuristic Data Dissemination for Mobile Sink Wireless Sensor Networks

MCCM: An Approach for Connectivity and Coverage Maximization

A Low-Latency and Energy-Efficient Neighbor Discovery Algorithm for Wireless Sensor Networks

Contact Info

Product

Resources

About