Energy Consumption Of Wireless Nodes Research Articles

Enhancing security & QoS of trust-enabled wireless networks using machine learning powered transformable blockchain sharding

Trust enabled wireless networks use temporal behaviour information of nodes in order to classify them into different trust categories. This information is utilized by the router for high performance communication that is optimized in terms of end-to-end delay, energy consumption, throughput, packet delivery ratio, and other quality of service (QoS) parameters. Establishing security in trust enabled wireless networks is a difficult task, because high trust nodes might be compromised by external or internal attacks, thereby disrupting normal communication. In order to perform this task, blockchain based security models are deployed. These models provide high transparency, comprehensive traceability, distributed processing, and data immutability, which makes them highly deployable for trust enabled networks. Blockchain models enforce compulsive verification of data before communication, which makes them resilient to DDoS, MITM, denial of service, and other data-based attacks. In order to enforce these checks, each of the block is hashed, and the hash values are compared with every existing block in the chain. These checks include hash uniqueness, and hash pattern validations; the later of which is decided by the network designer(s). As the length of blockchain increases, computational complexity of adding a new block (a.k.a. blockchain mining) increases exponentially, which adds to the end-to-end delay, and energy consumption of wireless nodes, which is a drawback of these models. To avoid this, sidechains & blockchain sharding models are developed. These models work by dividing the existing blockchain into multiple parts (based on a certain pre-set criteria), and then use the parts for high speed and low power mining. But again, due to increase in number of sidechains, the computational complexity of managing these chains, and locating data blocks within them increases exponentially. Moreover, in any practical wireless network, there is a need to communicate modifiable data, which is not supported by current blockchain implementations. In order to resolve these issues, this text proposes a transformable blockchain sharding model, which is managed via a light weight meta heuristic method for high-speed data access. The proposed model aims at reducing computational complexity of sidechain maintenance with the help of directed acyclic graphs (DAGs) for storing of hash ranges. The model also incorporates a transformable blockchain solution, wherein the block structure is designed to incorporate selectively mutable as well as non-mutable information. Both the mutable and non-mutable information is encrypted using high performance elliptic curve cryptosystem, which makes it highly secure against network attacks. The proposed model showcases 15% improvement in network lifetime, 8% reduction in end-to-end delay, 22% reduction in computational complexity, and 18% improvement in network throughput when compared with various blockchain and sidechain based wireless networks, thereby assisting in development of a high QoS and highly secure wireless network.

Wireless energy consumption optimisation using node coverage controlling in linear-type network

With the mushrooming development of automatic, intelligent and unmanned technologies, the application of wireless sensor network has become a hot topic. There is a kind of narrow-band structure, such as corridor or tunnel, which needs to use wireless sensor network for parameter sensing. To address unbalanced energy consumption of wireless nodes along length direction, this paper proposes a coverage controlling strategy, and linear-type network is built using several wireless nodes. A base station node is equipped in narrow band structures. Firstly, the wireless perception models are analysed aiming at special monitoring environments, which contain routing path, coverage model, link load and data credible rate; secondly, the survival lifetime of linear-type network is solved based on energy consumption of every wireless node; thirdly, considering the accidental death of deployed nodes, time-sharing scheduling strategy is designed to guarantee the stability of monitoring linear-type network; finally, the experimental results show that the proposed coverage control strategy can optimise the survival lifetime and the energy efficiency of linear-type network, which can provide the reference for node coverage in linear structures.

Wireless energy consumption optimisation using node coverage controlling in linear-type network

With the mushrooming development of automatic, intelligent and unmanned technologies, the application of wireless sensor network has become a hot topic. There is a kind of narrow-band structure, such as corridor or tunnel, which needs to use wireless sensor network for parameter sensing. To address unbalanced energy consumption of wireless nodes along length direction, this paper proposes a coverage controlling strategy, and linear-type network is built using several wireless nodes. A base station node is equipped in narrow band structures. Firstly, the wireless perception models are analysed aiming at special monitoring environments, which contain routing path, coverage model, link load and data credible rate; secondly, the survival lifetime of linear-type network is solved based on energy consumption of every wireless node; thirdly, considering the accidental death of deployed nodes, time-sharing scheduling strategy is designed to guarantee the stability of monitoring linear-type network; finally, the experimental results show that the proposed coverage control strategy can optimise the survival lifetime and the energy efficiency of linear-type network, which can provide the reference for node coverage in linear structures.

Modeling of Hybrid Energy Harvesting Communication Systems

Energy harvesting is considered as a prominent technology, particularly for low-power wireless nodes. In this paper, we propose the use of hybrid energy harvesting (HEH) utilizing multiple type energy sources and present the modeling of HEH communication systems based on their probabilistic natures. According to our approach, received energy levels of an HEH system for possible combinations of energy arrivals are characterized by using Gaussian mixture models, which are used to determine harvested energy levels. The range of harvested energy is partitioned, and probabilities of partitioning intervals are used to form a finite-state Markov energy channel (FSMEC) model as an energy channel. Similar to the energy arrival, we also include the probabilistic energy consumption of wireless node in this model, depending on multiple application services, by means of the FSMEC model. Thus, we develop an integrated Markov energy model for HEH communication systems corresponding to the energy harvesting and energy consumption profiles. To evaluate the performance of an HEH communication system, we derive the expressions of energy outage, energy shortage, and service loss probabilities analytically. In numerical studies, the derived expressions are verified by matching simulation results, and it is shown that the performance improves significantly with energy source diversity.

A collaborative task-oriented scheduling driven routing approach for industrial IoT based on mobile devices

Wireless sensor networks have been widely used in industrial IoT contexts due to their useful support during the production monitoring and risk pre-warning. However, complicated and extremely variable industrial environments call for higher communication stability and reliability to guarantee timely transmission of monitored data. Considering mobile intelligences such as Automated Guided Vehicles (AGVs), within industrial scenarios, we propose a solution based on hybrid (fixed and mobile) industrial wireless sensor networks featured with a task-oriented model. The proposal includes a heuristic modeling method adopted to assign tasks from definite orders which will be sent to the controller, and a collaborative routing algorithm exploiting the AGVs mobility on specific trajectories. Experiments have shown that, using mobility features of the AGVs, the integrated solution can promptly repair the network when a node and the relative link fail or the detected quality is low, significantly reducing the energy consumption of wireless nodes and data communication delay, thus improving the overall throughput and reliability of industrial IoT systems.

Low Probability Cluster Head Selection Energy Efficient Routing in MANET using DEEC

Adhoc Network is the prominent area of rese arch to make wireless networking more robust and reliable for packet delivery among nodes of a small or big network through wireless channel. The routing protocols are also designed to keep in mind that the energy consumption of wireless nodes should be as minimum as possible. In MANET most of the devices are battery operated and saving energy increases the lifetime of the node to transfer information over network, in such case the routing protocol plays important role to increase the network lifetime. In this paper an efficient routing methodology is proposed which significantly increases the network lifetime. Here the distributed energy efficient clustering (DEEC) routing protocol is used with low probability cluster head selection approach, which enhances the network lifetime better than simple DEEC.

Energy Effective Congestion Control for Multicast with Network Coding in Wireless Ad Hoc Network

In order to improve network throughput and reduce energy consumption, we propose in this paper a cross-layer optimization design that is able to achieve multicast utility maximization and energy consumption minimization. The joint optimization of congestion control and power allocation is formulated to be a nonlinear nonconvex problem. Using dual decomposition, a distributed optimization algorithm is proposed to avoid the congestion by control flow rate at the source node and eliminate the bottleneck by allocating the power at the intermediate node. Simulation results show that the cross-layer algorithm can increase network performance, reduce the energy consumption of wireless nodes and prolong the network lifetime, while keeping network throughput basically unchanged.

Energy-Efficient Cooperative Techniques for Infrastructure-to-Vehicle Communications

In wireless distributed networks, cooperative relay and cooperative multiple-input-multiple-output (MIMO) techniques can be used to exploit the spatial and temporal diversity gains to increase the performance or reduce the transmission energy consumption. The energy efficiency of cooperative MIMO and relay techniques is then very useful for the infrastructure-to-vehicle (I2V) and infrastructure-to-infrastructure (I2I) communications in intelligent transport system (ITS) networks, where the energy consumption of wireless nodes embedded on road infrastructure is constrained. In this paper, applications of cooperation between nodes to ITS networks are proposed, and the performance and the energy consumption of cooperative relay and cooperative MIMO are investigated and compared with the traditional multihop technique. The comparison between these cooperative techniques helps us choose the optimal cooperative strategy in terms of energy consumption for energy-constrained road infrastructure networks in ITS applications.

Power-aware routing based on DSR for Mobile Ad hoc NETworks

Energy consumption is a crucial design concern in Mobile Ad hoc NETworks (MANETs) since nodes are powered by batteries with limited energy, whereas Dynamic Source Routing (DSR) protocol does not take the energy limitation of MANET nodes into account. This paper proposes an energy-saving routing algorithm based on DSR: Power Aware Routing protocol based on DSR (PAR-DSR). The design objective of PAR-DSR is to select energy-efficient paths. The main features of PAR-DSR are: (1) Nodes use the Signal Attenuation Rate (SAR) to conduct power control operations; (2) Minimum path cost as metric to balance the traffic and energy consumption of wireless nodes. The simulation results show that PAR-DSR can greatly reduce the energy consumption of MANET nodes. The average node lifetime of PAR-DSR is 50%–77% longer than that of DSR.