Extending Lifetime of Wireless Sensor Network in Application to Road Traffic Monitoring

Self Cite

Wireless sensor networks have found many applications in detecting events such as security threats, natural hazards, or technical malfunctions. An essential requirement for event detection systems is the long lifetime of battery-powered sensor nodes. This paper introduces a new method for prolonging the wireless sensor network’s lifetime by reducing data transmissions between neighboring sensor nodes that cooperate in event detection. The proposed method allows sensor nodes to decide whether they need to exchange sensor readings for correctly detecting events. The sensor node takes into account the detection algorithm and verifies whether its current sensor readings can impact the event detection performed by another node. The data are transmitted only when they are found to be necessary for event detection. The proposed method was implemented in a wireless sensor network to detect the instability of cargo boxes during transportation. Experimental evaluation confirmed that the proposed method significantly extends the network lifetime and ensures the accurate detection of events. It was also shown that the introduced method is more effective in reducing data transmissions than the state-of-the-art event-triggered transmission and dual prediction algorithms.

Section: Experimental Testbedmentioning

confidence: 99%

Data Transmission Reduction in Wireless Sensor Network for Spatial Event Detection

Lewandowski

Płaczek

2021

Self Cite

“…Real-world examples of mechanisms for maximizing the network lifetime are manifold. They include, but are not limited to, smart agriculture monitoring [4], structural health monitoring [5], human activity monitoring [6], and road traffic monitoring [7]. Power-saving strategies such as the ones found in these examples can-according to [8]-be classified as belonging to one of the following groups:…”

Section: Introductionmentioning

confidence: 99%

“…Next, note that the objective function (Equation ( 2)) is only correct if the weight value of the dominating sets not utilized is set to zero. This is ensured by constraints (7). The remaining two sets of constraints, ( 8) and ( 9), are not required for the correctness of the ILP.…”

mentioning

confidence: 99%

A Population-Based Iterated Greedy Algorithm for Maximizing Sensor Network Lifetime

Bouamama

Blum

Pinacho-Davidson

2022

Finding dominating sets in graphs is very important in the context of numerous real-world applications, especially in the area of wireless sensor networks. This is because network lifetime in wireless sensor networks can be prolonged by assigning sensors to disjoint dominating node sets. The nodes of these sets are then used by a sleep–wake cycling mechanism in a sequential way; that is, at any moment in time, only the nodes from exactly one of these sets are switched on while the others are switched off. This paper presents a population-based iterated greedy algorithm for solving a weighted version of the maximum disjoint dominating sets problem for energy conservation purposes in wireless sensor networks. Our approach is compared to the ILP solver, CPLEX, which is an existing local search technique, and to our earlier greedy algorithm. This is performed through its application to 640 random graphs from the literature and to 300 newly generated random geometric graphs. The results show that our algorithm significantly outperforms the competitors.

“…In contrast, data processing consumes significantly less energy. Reduction of data transmission can lead to considerable energy savings and increased network lifetime [ 4 , 5 , 6 ]. Thus, effective methods are needed to avoid any unnecessary data transmissions in the wearable sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring

Lewandowski

Płaczek

Bernaś

2020

Self Cite

The recent development of wireless wearable sensor networks offers a spectrum of new applications in fields of healthcare, medicine, activity monitoring, sport, safety, human-machine interfacing, and beyond. Successful use of this technology depends on lifetime of the battery-powered sensor nodes. This paper presents a new method for extending the lifetime of the wearable sensor networks by avoiding unnecessary data transmissions. The introduced method is based on embedded classifiers that allow sensor nodes to decide if current sensor readings have to be transmitted to cluster head or not. In order to train the classifiers, a procedure was elaborated, which takes into account the impact of data selection on accuracy of a recognition system. This approach was implemented in a prototype of wearable sensor network for human activity monitoring. Real-world experiments were conducted to evaluate the new method in terms of network lifetime, energy consumption, and accuracy of human activity recognition. Results of the experimental evaluation have confirmed that, the proposed method enables significant prolongation of the network lifetime, while preserving high accuracy of the activity recognition. The experiments have also revealed advantages of the method in comparison with state-of-the-art algorithms for data transmission reduction.