Dispatching Rescue and Support Teams to Events Using Ad Hoc Networks and Fuzzy Decision Making in Rescue Applications

Khanmohammadi

2016

IET wirel. sens. syst.

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Routing protocols are used in wireless sensor network (WSN) to transmit data to a centre (e.g. a base station). In this study, the authors propose a routing protocol called dynamic three-dimensional fuzzy routing based on traffic probability to enhance network lifetime and increase packet delivery ratio. It uses a fuzzy-based procedure to transmit packets by hop-to-hop delivery from source nodes toward destination nodes. The proposed fuzzy system uses two input parameters including 'distance' and 'number of neighbours' and one output parameter denoted by 'traffic probability'. When a node has a sensed data or buffered data packet, it selects one of its neighbours, called chosen node, from among a list of candidate nodes (CNs). Candidates are the neighbours which have power energy higher than average remaining energy and free buffer more than average available buffer size. Distance and number of neighbours for each CN are fed in the fuzzy system to calculate traffic probability. The CN having the lowest traffic probability is selected as an appropriate chosen node to transmit packets to the destination. Simulation results show that the proposed protocol surpasses the greedy and A* heuristic routing for wireless sensor networks in home automation, dynamic optimal progress routing, and A-star & Fuzzy methods in terms of network lifetime and packet delivery ratio.

Section: Brief Description On Fuzzy Logicmentioning

confidence: 99%

“…Fuzzy logic is a mathematical model to utilise human reasoning in ambiguous mathematical problems [18, 19], which was first presented in the mid‐1960s by Lotfi‐Zadeh. Modelling and controlling the uncertain and complex systems in industry, nature, and humanity can be conducted using fuzzy systems.…”

Section: Brief Description On Fuzzy Logicmentioning

confidence: 99%

DFRTP: Dynamic 3D Fuzzy Routing Based on Traffic Probability in Wireless Sensor Networks

Khanmohammadi

2016

IET wirel. sens. syst.

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“…The proposed function estimates a cost for each path based on three parameters: cost of the path from start to each current node on the path, cost from current node to the straight line passing through start and target, and cost of the path from current node to target. It seems that T* algorithm can solve some of the existing problems in graph theory, communications (Gharajeh and Khanmohammadi, 2013;2015) and game theory (Nisan et al, 2007), etc.…”

Section: Introductionmentioning

confidence: 99%

T*: a weighted double-heuristic search algorithm to find the shortest path

2019

IJCSM

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This paper proposes a weighted double-heuristic search algorithm to find the shortest path between two points. It can be used in numerous fields such as graph theory, game theory, and network. This algorithm, called T*, uses a weighted and heuristic function as f(x) = × t(x) + × h1(x) + γ × h2(x). It selects the path which minimises f(x) where x is a current node on the path, t(x) is cost of the path from start to x, h1(x) is a heuristic to estimate the cost from x to the straight line passing through start and target, and h2(x) is a heuristic to estimate cost of the cheapest path from x to target. Furthermore, , , and γ indicate effective weights of each sub-function on f(x). T* algorithm is compared to the Greedy and A* algorithms in terms of hit rate and the number of processed nodes. Comparison results show that the proposed algorithm has a high efficiency compared to other algorithms.

“…Wireless sensor networks have some applications in environmental monitoring, traffic control, fire systems, etc. There are various challenges in these networks such as routing, fault tolerance, and overlapping . An appropriate strategy presented for WSNs tries to solve one or some of the existing problems.…”

Section: Introductionmentioning

confidence: 99%

“…There are various challenges in these networks such as routing, fault tolerance, and overlapping. [8][9][10][11][12][13][14] An appropriate strategy presented for WSNs tries to solve one or some of the existing problems. A schematic of a WSN for fire systems is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

An intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks

Khanmohammadi

2018

Int J Communication

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Summary Overlapping is one of the topics in wireless sensor networks that is considered by researchers in the last decades. An appropriate overlapping management system can prolong network lifetime and decrease network recovery time. This paper proposes an intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks, called IKOCP. This protocol uses some of the intelligent and knowledge‐based systems to construct a robust overlapping strategy for sensor networks. The overall network is partitioned to several regions by a proposed multicriteria decision‐making controller to monitor both small‐scale and large‐scale areas. Each region is managed by a sink, where the whole network is managed by a base station. The sensor nodes are categorized by various clusters using the low‐energy adaptive clustering hierarchy (LEACH)‐improved protocol in a way that the value of p is defined by a proposed support vector machine–based mechanism. A proposed fuzzy system determines that noncluster heads associate with several clusters in order to manage overlapping conditions over the network. Cluster heads are changed into clusters in a period by a suggested utility function. Since network lifetime should be prolonged and network traffic should be alleviated, a data aggregation mechanism is proposed to transmit only crucial data packets from cluster heads to sinks. Cluster heads apply a weighted criteria matrix to perform an inner‐cluster routing for transmitting data packets to sinks. Simulation results demonstrate that the proposed protocol surpasses the existing methods in terms of the number of alive nodes, network lifetime, average time to recover, dead time of first node, and dead time of last node.