Vahid Khalilpour Akram

An Energy-Efficient Distributed Cut Vertex Detection Algorithm for Wireless Sensor Networks

Maintaining connectivity is a very important objective of wireless sensor networks (WSNs) in successfully achieving data collection for applications. A cut vertex (node) is defined as a critical vertex whose removal disconnects a network component and partially disables data delivery. Hence, it is crucial that cut vertices be detected and treated with caution. In this paper, we propose an energy-efficient cut vertex detection algorithm for WSNs. Our algorithm uses a depth-first search approach and is completely distributed. It benefits from the radio multicast capabilities of sensor nodes and is the first algorithm with O(N) time complexity and O(N) sent message complexity, in which each message is O(log2(N)) bits. We show the operation of the algorithm, analyze it in detail, provide testbed experiments and extensive simulations. We compare our proposed algorithm with the other cut vertex detection algorithms and show that our algorithm saves up to 6.8 times more energy in less time.

Breadth-First Search-Based Single-Phase Algorithms for Bridge Detection in Wireless Sensor Networks

Wireless sensor networks (WSNs) are promising technologies for exploring harsh environments, such as oceans, wild forests, volcanic regions and outer space. Since sensor nodes may have limited transmission range, application packets may be transmitted by multi-hop communication. Thus, connectivity is a very important issue. A bridge is a critical edge whose removal breaks the connectivity of the network. Hence, it is crucial to detect bridges and take preventions. Since sensor nodes are battery-powered, services running on nodes should consume low energy. In this paper, we propose energy-efficient and distributed bridge detection algorithms for WSNs. Our algorithms run single phase and they are integrated with the Breadth-First Search (BFS) algorithm, which is a popular routing algorithm. Our first algorithm is an extended version of Milics algorithm, which is designed to reduce the message length. Our second algorithm is novel and uses ancestral knowledge to detect bridges. We explain the operation of the algorithms, analyze their proof of correctness, message, time, space and computational complexities. To evaluate practical importance, we provide testbed experiments and extensive simulations. We show that our proposed algorithms provide less resource consumption, and the energy savings of our algorithms are up by 5.5-times.

Energy-Efficient Bridge Detection Algorithms for Wireless Sensor Networks

A bridge is a critical edge whose fault disables the data delivery of a WSN component. Because of this, it is important to detect bridges and take preventions before they are corrupted. Since WSNs are battery powered, protocols running on WSN should be energy efficient. In this paper, we propose two distributed energy-efficient bridge detection algorithms for WSNs. The first algorithm is the improved version of Pritchards algorithm where two phases are merged into a single phase and radio broadcast communication is used instead of unicast in order to remove a downcast operation and remove extra message headers. The second algorithm runs proposed rules on 2-hop neighborhoods of each node and tries to detect all bridges in a breadth-first search (BFS) execution session using O(N) messages with O ( Δ ( log 2 ( N ) ) ) bits where N is the node count and Δ is the maximum node degree. Since BFS is a natural routing algorithm for WSNs, the second algorithm achieves both routing and bridge detections. If the second proposed algorithm is not able to to classify all edges within the BFS phase, improved version of Turaus algorithm is executed as the second phase. We show the operation of the algorithms, analyze them, and provide extensive simulation results on TOSSIM environment. We compare our proposed algorithms with the other bridge detection algorithms and show that our proposed algorithms provide less resource consumption. The energy saving of our algorithms is up to 4.3 times, while it takes less time in most of the situations.

An energy-efficient, self-stabilizing and distributed algorithm for maximal independent set construction in wireless sensor networks

Abstract Maximal independent set (MIS) is a very important structure that provides data aggregation, topology control and routing for wireless sensor networks (WSNs). Energy-efficient and fault-tolerant construction of MIS on WSNs is one of the vital tasks. A distributed sensor network is self-stabilizing if it can initially start at any state and regain a legal state in a finite time without any external intervention. Self-stabilization is a considerable method to provide fault tolerance in WSNs. This paper presents a distributed self-stabilizing MIS algorithm which is an improved version of Turau’s algorithm under a fully distributed scheduler for WSNs. The proposed algorithm is theoretically analyzed and evaluated with its counterparts. The proposed algorithm is compared with the other studies through testbed experiments on IRIS nodes and simulations on TOSSIM environment. It is shown that the proposed algorithm outperforms other algorithms in terms of move count and energy consumption.

DECK: A Distributed, Asynchronous and Exact k -Connectivity Detection Algorithm for Wireless Sensor Networks

Abstract Wireless Sensor Networks (WSNs) are one of the widespread platforms for communications and remote sensing. A robust WSN should tolerate the failures of nodes without losing the connection to active nodes. A network is k-connected if all active nodes remain connected after failures in k-1 arbitrary nodes. Finding (detecting) the k value in a WSN is a significant operation to estimate the connectivity robustness, reliability and load balancing level of the network. Also, the detection of k values provides useful information for connectivity restoration, lower bound of node degree, critical nodes and possible cycles. In this paper, we propose an asynchronous distributed algorithm (DECK) for k-connectivity detection in WSNs. In the proposed algorithm, each node estimates a local k using its 2-hop neighborhood information and then a distributed linked list of minimum estimations is created between the nodes. Finally, the sink node validates the correctness of detected values by finding the number of node-disjoint paths to the node having the minimum estimation. We analyze our algorithm in detail, provide theoretical analysis, testbed experiments on the IRIS nodes and simulation results in the TOSSIM simulator by comparing with the other algorithms. The comprehensive testbed and simulation results show that the proposed algorithm always finds exact k values with reasonable energy consumption while the correct detection ratios of existing distributed algorithms on similar networks are usually less than 40%.

PACK: Path coloring based k-connectivity detection algorithm for wireless sensor networks

Abstract A k-connected wireless sensor network (WSN) can tolerate failures on k-1 arbitrary nodes without loosing the connectivity between the remaining active nodes. Hence, the k value is one of the useful benchmarks that can help to measure the network reliability. Given that the nodes in a k-connected network has at least k disjoint paths to each other, we propose the path coloring based k-connectivity detection algorithm (PACK) that finds the k by counting the disjoint paths between the sink and all other nodes. The proposed algorithm has two Detection and Notification phases. In the Detection phase, all nodes find their disjoint paths to the sink and in the Notification phase the minimum detected path count, which determines the global k, is sent to the sink node. We theoretically prove that the detection range of our proposed algorithm is better than the existing distributed algorithms and uses fixed length messages with O(nΔlog2n) bit complexity and O(n) time complexity where n is the number of nodes and Δ is the maximum node degree. According to the comprehensive simulation results, the average correct detection ratio of proposed algorithm is more than 91% which is at least 2.3 times higher than the existing algorithms.