Mobile Ad Hoc Network Energy Cost Algorithm Based on Artificial Bee Colony

Tareq, Mustafa; Alsaqour, Raed; Abdelhaq, Maha; Uddin, Mueen

doi:10.1155/2017/4519357

Cited by 25 publications

(23 citation statements)

References 12 publications

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“…A node in DTN will hold and retain the packet until a path or connection is available as the buffer management policy is dependent on combination of packet scheduling to cope with the problem of limited encounter opportunity between communication nodes [7]. In [8], [8], [9] MANET, nodes will discard the packet if not found a connected link to the destination. On the other hand, when a node in MANET receives packets, it will decide which packet has the shortest route available at the moment.…”

Section: Methodsmentioning

confidence: 99%

Enhanced Buffer Management Policy and Packet Prioritization for Wireless Sensor Network

Rabileh

Bakar

Mohamed³

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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Limited storage of the sensor node is one of the main causes of packet drop in the Wireless sensor network (WSN). Frequent link disconnection is also another cause of packet drop. Due to lack of continues end-to-end connection; neighbouring nodes might not be able to connect with one another. Hence, communication may be setup with the help of store and forward approach between the source and destination. In that case, the sensor node may not be capable of storing a chunk of data since the buffer is available in a small amount. In order to store the data packets in the buffer when the link is down, an effective buffer management scheme is highly needed to keep the data packets for a long time until the link is re-established. This paper proposes a new buffer management scheme called Packet Priority Heterogonous Queue (PPHQ), which based on prioritizing and classifying the packets into different categories to minimize the loss of important packets. Unlike the existing Multi-layer WSN, that treats the data packets differently; we considered the data packets such as; temperature, humidity, and pressure to be same. However, the classification of different packet types is based on the sensor's information value. We completely divide the whole buffer into different queues, and thus the newly arrived packets are inserted in their corresponding queue. The buffer will then prioritize and schedule on which packet to be stored or transmit first when the buffer is overloaded. Our result exhibits that PPHQ scheme indeed provides minimum packet drop as well as maximum throughput compared to existing Multi-layer WSN buffer management schemes.

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Section: Methodsmentioning

confidence: 99%

Enhanced Buffer Management Policy and Packet Prioritization for Wireless Sensor Network

Rabileh

Bakar

Mohamed³

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

show abstract

“…It is a straightforward and reactive routing strategy that can be applied in wireless ad hoc networks [32][33][34]. It differs from other protocols as DSR is based on the source routing principle, indicating that the node that owns the data to send must identify and know a multi-hop route to the receiver node [34][35][36].…”

Section: Dynamic Source Routing (Dsr) Protocolmentioning

confidence: 99%

“…If the source finds multiple routes, the route with the minimum hop count will be selected and added as a header of the transmitted data packet [34,35]. Otherwise, two essential phases based on route discovery and maintenance will be established [32,34,37,38].…”

Section: Dynamic Source Routing (Dsr) Protocolmentioning

confidence: 99%

“…The other mechanism used in DSR is route maintenance, which is applied to find alternative routes to continue the transmission of data packets when the current path experiences failure. In this stage, the source receives a route error (RERR) packet and then deletes the expired route from its cache to start a new search for a new route [32]. Figure 2 depicts the route maintenance mechanism.…”

Section: Dynamic Source Routing (Dsr) Protocolmentioning

confidence: 99%

“…The simulation results showed that the recommended BeeIP provided a powerful adaptability level as well as efficiency in terms of the selection of suitable route decisions. Tareq et al [32] introduced an energy cost Bee DSR (BEE-DSR) algorithm based on integrating the artificial bee colony (ABC) optimization method with the DSR. BEE-DSR is applied to determine the optimal route by considering the level of energy of intermediate nodes and hop count.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A novel dynamic source routing (DSR) protocol based on minimum execution time scheduling and moth flame optimization (MET-MFO)

Almazok

Bilgehan

2020

J Wireless Com Network

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In general, the conventional routing approaches in mobile ad hoc networks (MANETs) provide a route between the source and the target with a minimum hop count. Dynamic source routing (DSR) as a typical prototype of routing protocols relies on the minimum hop count parameter to provide the path without considering any other factors such as energy consumption and node energy level, which significantly affect the routing algorithm performance. To enhance the performance of the DSR, a novel and efficient routing mechanism based on a hybrid approach using the minimum execution time (MET) scheduling and moth flame optimization (MFO) scheme is proposed. This hybridization version of DSR is called the (MET-MFODSR) algorithm, and it aims to improve the routing mechanism through establishing an optimal route with minimum energy consumption, which increases the network lifetime and reduces route failure issues. The proposed MET-MFODSR protocol is implemented using the MATLAB platform, analyzed and evaluated in different simulation environments. The simulation results demonstrate that the suggested routing algorithm is applicative and practicable, and its performance exceeds the performance of the existing Bee DSR (BEEDSR) and Bee-inspired protocol (BeeIP) algorithms.

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An enhanced distance and residual energy‐based congestion aware ant colony optimization routing for vehicular ad hoc networks

Ramamoorthy

Thangavelu²

2022

Int J Communication

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Summary Due to the ad hoc nature of vehicular ad hoc networks (VANETs), routing in VANETs is challenging. Using an energy‐efficient shortest path without congestion (EESPWC) increases routing efficiency by satisfying specific parameters (high throughput, high packet delivery ratio, low overhead, low end‐to‐end delay, low packet loss ratio, and low energy consumption). In this work, enhanced distance and residual energy‐based congestion aware ant colony optimization (EDR‐CAACO) for VANETs is proposed to find an EESPWC. The proposed EESPWC produces high throughput and high packet delivery ratio with low overhead and low delay. In EDR‐CAACO, the roulette wheel selects the EESPWC based on the link's fitness values. The fitness value is computed based on the pheromone level of the link. The enhanced ACO (ant colony optimization) of the proposed model relies on the distance, residual, and congestion levels of the vehicles to estimate pheromone levels. For path selection, the roulette wheel relies on links that have the highest fitness value. The combination of high fitness value links provides an energy‐efficient non‐congested shortest route. The simulation results prove that the overall performance of EDR‐CAACO is better than Improved Distance Based Ant Colony Optimization Routing (IDBACOR), Fuzzy‐based Ant colony optimization (F‐ANT), ant colony optimization routing algorithm (ARA), and AntNet in terms of throughput, routing overhead, packet delivery ratio, end to end delay, and packet loss rate.

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Mobile Ad Hoc Network Energy Cost Algorithm Based on Artificial Bee Colony

Cited by 25 publications

References 12 publications

Enhanced Buffer Management Policy and Packet Prioritization for Wireless Sensor Network

Enhanced Buffer Management Policy and Packet Prioritization for Wireless Sensor Network

A novel dynamic source routing (DSR) protocol based on minimum execution time scheduling and moth flame optimization (MET-MFO)

An enhanced distance and residual energy‐based congestion aware ant colony optimization routing for vehicular ad hoc networks

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