A channel-aware expected energy consumption minimization strategy in wireless networks

With the wide deployment of mobile network, it’s very important to utilize mobile networks resource efficiently and decrease energy consumption of wireless terminals for constructing green mobile computing. This paper mainly studies minimization problem of energy consumption in data transmission under the given data generation rate and transmission delay demand in wireless links. Firstly, a finite horizon optimal stopping problem with transmission data quantity constraint and energy consumption minimization is constructed, and then we prove the existence of optimal stopping rule and offer the solutions and processes. At last, we obtain the optimal transmission rate threshold of the sending terminal for each channel detection slot time, so as to form the optimization strategy of energy consumption for data transmission based on optimal stopping theory. The simulation results show that the strategy proposed by this paper has lower average energy consumption and higher average delivery success ratio than other strategies, and achieves better optimization effect in energy consumption.

SOHCL-RDT: A self-organized hybrid cross-layer design for reliable data transmission in wireless network

The widespread adoption of Autonomous Vehicles (AVs) necessitates Mobile Edge Computing (MEC) to offload computation-intensive tasks, thereby minimising energy consumption while maintaining real-time responsiveness for critical operations such as obstacle avoidance, lane keeping, adaptive cruise control, and intersection management. However, optimising task offloading and resource allocation poses significant challenges due to the AVs' constrained battery life and limited processing capacity. To tackle this, we propose a unified framework integrating Optimal Stopping Theory (OST), Deep Reinforcement Learning (DDPG), and a Genetic Algorithm (GA). OST determines the optimal offloading checkpoint, while DDPG dynamically optimises resource allocation to minimise energy consumption and ensure rapid responsiveness for realtime autonomous operations. Additionally, GA enhances MEC efficiency by optimising the execution order in scenarios where multiple tasks need to be offloaded. Simulation results show that OST improves offloading performance by at least 9.4% compared to conventional strategies. The DDPG-based approach reduces vehicle energy consumption by at least 24.7%, while the GA method decreases queue waiting times by 67.6% and reduces failed offloading tasks by 20%. This framework effectively balances energy efficiency and task latency while ensuring reliable and responsive AV operations in the MEC-assisted vehicular network.

Wireless sensor networks (WSNs) are composed of many low cost, low power devices with sensing, local processing and wireless communication capabilities. Recent advances in wireless networks have led to many new protocols specifically designed for WSNs where energy awareness is an essential consideration. Most of the attention, however, has been given to the routing protocols since they might differ depending on the application and network architecture. Minimizing energy dissipation and maximizing network lifetime are important issues in the design of routing protocols for WSNs. In this paper, the low-energy adaptive clustering hierarchy (LEACH) routing protocol is considered and improved. We propose a clustering routing protocol named intra-balanced LEACH (IBLEACH), which extends LEACH protocol by balancing the energy consumption in the network. The simulation results show that IBLEACH outperforms LEACH and the existing improvements of LEACH in terms of network lifetime and energy consumption minimization.

In smart grid, Advanced Metering Infrastructure (AMI) systems feed the metering data to Energy Supplier (ES) every 15 minutes. Using this information, ES can offer incentives to reduce the total power consumption during the peak hours through the real-time pricing mechanism. However, it also brings privacy concerns. Since the metering data is collected by the ES nearly in real-time, the customer's behavior and habits can be disclosed via the statistical patterns. The privacy problem will hamper the development and deployment of future smart grid. In this paper, we propose a novel metering data transmission scheme in wireless networks. In each Smart Meter (SM), the metering data is split into two parts randomly: Part 1 and Part 2, where the sum of two parts is equal to the original metering data. Part 1 is sent to the ES via cellular link, and Part 2 is sent to the next SM via Wireless Sensor Network (WSN) link. Then the next SM merges Part 2 of the previous SM into its original metering data, and creates the updated metering data. Using the proposed scheme, the metering data received by the ES is randomized, and the customer's privacy can be effectively protected even if some SM cooperates with the ES.

This paper proposes a location-based MAC (LMAC) scheme for wireless networks with randomly placed nodes. This scheme regulates channel access by sharing local location information among transmitters. A lattice approximation approach is used to derive upper and lower bound for the success probability for a typical transmission attempt. Numerical results show that with the node density and link distance fixed, the optimal LMAC provides a much higher density of successful transmissions than CSMA and ALOHA.

In this paper, performance of cognitive transmission over time-selective flat fading channels is studied under quality of service (QoS) constraints and channel uncertainty. Cognitive secondary users (SUs) are assumed to initially perform channel sensing to detect the activities of the primary users, and then attempt to estimate the channel fading coefficients through training. Energy detection is employed for channel sensing, and different minimum mean-square-error (MMSE) estimation methods are considered for channel estimation. In both channel sensing and estimation, erroneous decisions can be made, and hence, channel uncertainty is not completely eliminated. In this setting, performance is studied and interactions between channel sensing and estimation are investigated. Following the channel sensing and estimation tasks, SUs engage in data transmission. Transmitter, being unaware of the channel fading coefficients, is assumed to send the data at fixed power and rate levels that depend on the channel sensing results. Under these assumptions, a state-transition model is constructed by considering the reliability of the transmissions, channel sensing decisions and their correctness, and the evolution of primary user activity which is modeled as a two-state Markov process. In the data transmission phase, an average power constraint on the secondary users is considered to limit the interference to the primary users, and statistical limitations on the buffer lengths are imposed to take into account the QoS constraints of the secondary traffic. The maximum throughput under these statistical QoS constraints is identified by finding the effective capacity of the cognitive radio channel. Numerical results are provided for the power and rate policies.

Sensor nodes are randomly deployed to perform specific area monitoring in geographical region and temporal space. The network connectivity maintenance is a major requirement for accurate event detection with minimum energy consumption. To minimize the energy consumption, various clustering algorithms have been evolved in research studies. But, they failed to consider the other performance parameters such as quality of service constraints and the performance level. The initialization of nodes nearer to the base station (BS) as relay nodes reduces the number of relay node participation and increases the performance. This paper proposes the novel ant colony meta-heuristic based unequal clustering for the novel cluster head (CH) selection. The data fusion from the CH node to the intermediate node called Rendezvous node reduces the message transmissions and hence the energy consumed by the nodes is minimum. The neighbor finding phase and the link maintenance through the Meta-Heuristic Ant Colony Optimization approach selects the optimal path between the nodes which increases the packets delivered to the destination. The population initialization requires more time at this stage. Hence, the Haversine distance is estimated among the nodes which also reduces the dimensionality of the message transmission among the nodes. The prediction of optimal path and the CH selection using Ant Colony Optimization Meta-Heuristic and unequal clustering reduces the energy consumption effectively. The comparative analysis of proposed Meta-Heuristic Ant Colony Optimization based Unequal Clustering with the existing unequal clustering approaches on the basis of various performance parameters such as Packet Delivery Ratio, number of packets sent to the BS, energy consumption, residual energy and the percentage of dead nodes shows the effectiveness of proposed work in WSN applications.

Energy bills are on the rise and with the recent attention to saving the global environment. Saving energy (minimizing energy consumption) is becoming a standard issue for all industrial and commercial applications. Moreover, provisioning of quality of service (QoS) for multimedia traffic in wireless networks is complicated due to user mobility and limited wireless resources. Bandwidth (throughput) and hop count and are the important parameters in QoS requirements. In this article, a novel QoS constrained minimum power cellular ad hoc augmented network (QCMP CAHAN) architecture is proposed for next generation wireless networks. The QCMP CAHAN architecture is proposed to find the optimal minimum power route under bandwidth and hop-count constraints (QoS constraints). The QCMP CAHAN has a hybrid architecture, in which each mobile terminal (MT) of CDMA cellular networks has ad hoc communication capability. The QCMP CAHAN is an evolutionary approach to traditional cellular networks. We show that the total energy consumed by the MTs is lower in the case of QCMP CAHAN than in the case of traditional cellular networks. As the ad hoc communication range of each MT increases, the total transmitted energy in QCMP CAHAN decreases. However, due to the increased number of hops involved in information delivery between source and destination, the end-to-end delay increases. The maximum end-to-end hop count will be limited to a specified tolerable value, and QCMP CAHAN has ability to adapt to various hop-count constraints for different services. A MT in QCMP CAHAN will not relay any message when its ad hoc communication range is zero, and if this is the case for all MTs, then QCMP CAHAN reduces to the traditional cellular networks. Moreover, the bandwidth constrained problem in QCMP CAHAN is described as a nonlinear programming problem to minimize the total power consumption. We solved the bandwidth constrained problem in QCMP CAHAN by allocating optimum traffic rates on different routes between source and destination.

In cognitive radio networks, primary users (PUs) can leverage secondary users (SUs) as cooperative relays to increase their transmission rates, and SUs will in turn obtain more spectrum access opportunities. While most existing works assume that SUs are passively selected by PUs regardless of SUs' willingness, in this paper, we propose a cooperative strategy for SUs to actively decide whether to cooperate or not. Basically, due to PUs' time-varying traffic demands, it is essential for SUs to firstly observe the channels and then select a specific PU to cooperate with in order to save the energy. In our paper, this decision related problem is formulated based on optimal stopping theory where SUs observe PUs in time sequence and then make decisions whether to stop observation and cooperate right away or wait till next time slot to repeat the same process. We address this problem by using backward induction and derive the energy-efficient strategy for SUs. To validate the feasibility of our proposed scheme, extensive simulations are conducted to show the impact of PUs' traffic demands on SUs' decisions. The results also reveal that the proposed optimal rule outperforms the greedy selection strategy and is thus more energy- efficient to be applied to the cooperative cognitive radio networks.

The Global energy consumption and carbon footprint related to operating mobile phones in wireless networks is increasing significantly. In order to determine the overall energy consumption of a wireless network, both the operation of the network infrastructure and the devices connected to that network must be considered. Although the larger part of the global energy consumption of wireless networks is consumed at base station sites and access points, a significant part is consumed from the operation of mobile phones. In this paper, system level power consumption models for mobile phones in terms of 2G, 3G and Wi-Fi are presented. The developed model increases the accuracy of the current power profiles by considering different stages of a single transmission, including the variables affecting each stage. The power states of wireless interfaces, maintenance, network attachment/detachment and network resource allocation policies of different network operators are also considered to obtain a complete model that can accurately predict that power consumption at every stage of connectivity. A comparison between power consumption of different radio access technologies is also included to promote energy efficient use of spectrum. Using the system level power models presented in this paper, the contribution of the operation of mobile phones to overall energy consumption of wireless communication networks can be determined.

TOT: Trust aware opportunistic transmission in cognitive radio Social Internet of Things

With the rapid development of wireless network technology, wireless networks have been widely used in many fields due to the flexibility of wireless access. However, in the field of video transmission, because the link state is instability and the transmission is easily interfered in wireless networks, the quality of video transmission is affected badly. In fading and shadowing channels, some users can’t watch the video normally. This paper provides an adaptive QoS solution of real-time video stream in wireless network, by adding a relay server it can provide multicast or unicast video stream satisfying multiple QoS for users with different network state. This solution has been successfully applied in Qingdao Public safety emergency video surveillance system.

Optimal stopping theory studies problems that involve determining the best time to intervene and stop a process in order to maximize expected rewards or minimize expected costs. Applications of optimal stopping theory are plentiful and include asset trading (e.g., the best time to sell an asset), derivative pricing (e.g., American options), real options theory (e.g., the best time to invest in a project), economics of gambling (e.g., when to stop gambling in a casino), and search and matching (e.g., when to stop searching and accept a job). In this chapter we briefly summarize standard optimal stopping theory in discrete time.

In wireless sensor networks, the mobile agent technology is used in data transmission from one cluster to another cluster. The Optimization of mobile agent in the routing within the clustering algorithm for wireless sensor networks to further reduce the amount of data transfer. the main objectives of wireless sensor network design is to improve the energy efficiency & effective utilization of sensor nodes to prolong the lifespan of a wireless network .This paper presents an effective clustering algorithm, which is an extension to the LEACH routing algorithm., this algorithm fully utilizes the location information of network nodes in routing to reduce the routing cost. Through a clustering approach of intelligent mobile agents, it is possible to reduce the cost of time for each individual agent, here the problem of Multiple Clustering of Mobile Agents (MCCMA) where the decision is to cluster mobile agents such that a group of similar intelligent mobile agents will visit a group of similar sensor nodes are compare with location based clustering algorithm of wsn. both of Simulation results indicate that these algorithm & approach of mobile agent & wireless sensor network Nodes can have balance nodes' energy consumption and prolong the network's life span. It also has good extensibility & stability