Balancing Energy Consumption and Latency in Vehicle Edge Computing for 6G Networks

In recent years, the Edge Computing (EC) paradigm has emerged as an enabling factor for developing technologies like the Internet of Things (IoT) and 5G networks, bridging the gap between Cloud Computing services and end-users, supporting low latency, mobility, and location awareness to delay-sensitive applications. An increasing number of solutions in EC have employed machine learning (ML) methods to perform data classification and other information processing tasks on continuous and evolving data streams. Usually, such solutions have to cope with vast amounts of data that come as data streams while balancing energy consumption, latency, and the predictive performance of the algorithms. Ensemble methods achieve remarkable predictive performance when applied to evolving data streams due to several models and the possibility of selective resets. This work investigates a strategy that introduces short intervals to defer the processing of mini-batches. Well balanced, our strategy can improve the performance (i.e., delay, throughput) and reduce the energy consumption of bagging ensembles to classify data streams. The experimental evaluation involved six state-of-art ensemble algorithms (OzaBag, OzaBag Adaptive Size Hoeffding Tree, Online Bagging ADWIN, Leveraging Bagging, Adaptive RandomForest, and Streaming Random Patches) applying five widely used machine learning benchmark datasets with varied characteristics on three computer platforms. As a result, our strategy can significantly reduce energy consumption in 96% of the experimental scenarios evaluated. Despite the trade-offs, it is possible to balance them to avoid significant loss in predictive performance.

In recent years, mobile sinks are used more and more efficiently in sensor networks to collect data for the mobility advantage in balancing energy consumption than static sinks. However, it is still a challenge in both efficiency and network cost to avoid generating large amounts of overheads and lots of unnecessary energy consumption, when data source forward sensing data to mobile sink proactively according to their location information broadcasted all over the network. To reduce the overhead and balance the energy consumption in a network, we propose a clue-based data collection routing (CBDCR) protocol for mobile sensor networks. In CBDCR, a mobile sink moves randomly other than the following predesigned trajectories, during which it only broadcasts its location messages by limited hops instead of the whole network. The nodes getting these messages are called watchers who can obtain the upstream or downstream relations and infer the hop(s) from them to the mobile sink, and then a watcher stores this information as a “clue” to the location of mobile sink for data forwarding. As the movement of the mobile sink, more and more nodes are becoming watchers, and so a sensing data can be efficiently forwarded to the mobile sink according to these clues. Numerous simulations are conducted with mobile sinks in network to evaluate the performance of CBDCR, which demonstrate that CBDCR can both reduce the redundant transmission messages significantly and balance the network energy consumption.

Joint edge caching and dynamic service migration in SDN based mobile edge computing

In recent years, SDWSNs has been gaining more and more attention in the wireless sensor networks (WSNs) space. This phenomenon is mainly attributed to that both data collection and definable control can be more clearly achieved. However, simultaneously, there are some defects in energy balance and energy efficiency in SDWSNs, especially in the case of all nodes with limited energy. Large amounts of data reception will cause the rapid energy consumption in the aspect of energy of the controller, which will quickly lead to network failure. In this paper, to balance energy consumption of network in which energy of all nodes are limited and prolong lifetime of the network, we propose a novel balancing energy consumption algorithm based-on controller handover. In proposed algorithm, BP neural network is applied to support the mechanism of handover. Simulation results show that this algorithm can effectively balance energy consumption and extend lifetime of the network compared with the traditional energy saving algorithms in SDWSNs.

For the sake of avoiding the data abundance and balancing the energy consumption in wireless sensor networks, a data fusion clustering hierarchy based on data fusion (CHDF) is proposed. The clustering module and multi-hop tree are designed for data transmission. The result of simulation experiment shows that the proposed CHDF can balance energy consumption and prolong the lifetime of network.

Analysis of cumulative energy consumption in an oxy-fuel combustion power plant integrated with a CO2 processing unit

A group of collaborative nodes can efficiently complete spatial long-distance transmission tasks using beamforming technology. However, a high sidelobe level interferes with communication quality, and uneven energy consumption of nodes affects network lifetime. This paper proposes an energy-efficient collaborative transmission algorithm based on potential game theory for beamforming. First, the minimum number of cooperative nodes is determined in accordance with the energy consumption and spacing limitation condition. A group of nodes satisfying the node spacing condition is selected as cooperative nodes based on the ring array to minimize communication interference among nodes. Second, a potential game model is proposed as a joint method for optimizing the collaborative parameters of the cooperative nodes and their energy consumption balancing features. Finally, the game process is continuously executed until the Nash equilibrium is reached. According to simulation results, the sidelobe level caused by the cooperative nodes is reduced and the transmission conflicts are lessened. Thus, the quality of communication links in between nodes in the network is improved. Energy efficiency is also promoted because a balancing of energy consumption is involved in the proposed potential game model, and network lifetime is effectively prolonged accordingly.

Many mission critical devices are increasing with upcoming 5G network to fulfill a low latency for a real time network service on smart factory, autonomous vehicle, etc. Distributed cloud computing system also has a key role to execute the various mobile devices, because, an edge computing is the nearest from the mobile devices to provide low latency and computation energy consumption. In this paper, we consider the autonomous vehicles with video live streaming services. Especially, the vehicles require a low transmission delay as within 10 ms. To reduce a latency with low energy consumption, we propose a service chaining offloading decision with a deep reinforcement learning. We split tasks of the vehicle per service function blocks which have their own role. So it can do partial offloading and user association in a On-Device Edge of the vehicle and in the SBS at the same time. We can get results that service chaining offloading decision gives more optimal energy consumption with low-latency to autonomous vehicle users.

In a wireless sensor network, reducing the energy consumption of sensor nodes and increasing energy conservation can be considered as two significant factors to prolong the network longevity. In this scheme, we proposed an energy-efferent data dissemination algorithm for the sensor nodes by making a virtual hexagonal cell-based infrastructure and multi-mobile sink to create a balance in energy consumption and to mitigate the energy-hole problem. The basic idea of the algorithm is to provide the ability for some of the sensing nodes that are elected by the virtual hexagonal backbone to inform other sensing nodes about the latest location of the nearest mobile sinks to decrease the overhead of updating the mobile sink location for the sensing nodes. Moreover, the sensor nodes can benefit considerably from these specified nodes as a relay node in the multi-hop routing process to send the data to the closest mobile sink based on the latest sink position information, which in turn leads to high energy conservation and low communication overhead between the sensing nodes and the mobile sinks in the network. According to the simulation results, the performance of the proposed scheme is better than the existing data transmission approaches in terms of total energy consumption, delay, and network longevity.

Hybrid wireless mesh networks are suitable for complex environment communication in coal mine. Mesh clients with application service and routing function in hybrid wireless mesh networks can form a highly robust hybrid network with mesh routers. The processes of nutrient flux transfer and path choice in Physarum network are similar to data transmission and routing selection in hybrid wireless mesh networks. In this article, we use Physarum-inspired autonomic optimization model to design a Physarum-inspired multi-parameter adaptive routing protocol to improve the service quality of coal mine hybrid wireless mesh networks. Physarum-inspired multi-parameter adaptive routing protocol has achieved distributed routing decision by drawing the hybrid wireless mesh network parameters into Poisson’s equation of Physarum-inspired autonomic optimization model to measure the quality of link and implements two adjustment strategies to make the protocol more adaptive. The resource-dependent adjustment, which considers the irreversible energy consumption and recoverable buffer occupation, makes the energy consumption problem prominent when there is a lack of energy. The position-dependent adjustment makes routing decision efficient according to the load of different positions, which is caused by many-to-one data transmission model in coal mine. Based on NS2, simulation experiments are performed to evaluate the performance of Physarum-inspired multi-parameter adaptive routing protocol, and the results are compared with those of ad hoc on-demand distance vector, HOPNET, ANT-DSR, and Physarum-inspired routing protocols. The experimental results show that the route path selected by Physarum-inspired multi-parameter adaptive routing protocol is better than those selected by the other four protocols in the performance of average end-to-end delay and delivery ratio. The balance of energy consumption and network load is achieved and the network lifetime is effectively prolonged when using Physarum-inspired multi-parameter adaptive routing protocol.

Coal heavy-haul railway has been aiming at maximizing capacity utilization, but ignoring energy consumption for a long time. With the focus on green production, heavy-haul railways need transportation organization plans that can balance energy consumption and capacity utilization. Based on this, this paper proposes a data mining + optimization framework that uses train trajectory data to estimate train energy consumption and then uses a mixed integer programming model to simultaneously optimize plans from energy and capacity aspects. We use Gaussian distribution to describe features of energy consumption under different situations, and build a multi-dimensional cube to store these features to connect with the optimization model. In addition, a branch-and-bound algorithm is design to solve the optimization model. From the sensitivity analyses we can conclude that (1) shortening the departure interval from 13 min to 9 min will generate more energy consumption, about 3.6%; (2) combining short-form trains (50 units) with long-form trains (100 units) while increasing the carrying capacity will generate more energy consumption, about 5~14%; and (3) by controlling weights of the optimization model, capacity–energy-balanced plans can be obtained. The results can contribute to improving the sustainability of railways.

Balanced energy consumption in WSNs is the key to maximize network lifetime. After analyzing laws of energy consumption of WSNs, a heterogeneous node deployment model based on area topology control is proposed in this paper. It utilizes predetermined heterogeneous node deployment to balance area energy consumption ratio and the topology control algorithm based on area energy consumption ratio to maintain dynamic equilibrium energy consumption. Simulation results show that our model takes a superior performance on maintaining network coverage, balancing energy consumption, and maximizing network lifetime. Compared with similar WSNs deployment models, our model can be more efficient to prolong network lifetime and has advantages in reducing dependence on deployment accuracy, balancing dynamic energy consumption, and improving lifetime in poor network environment.

DCFH: A dynamic clustering approach based on fire hawk optimizer in flying ad hoc networks

Recently, the study of multicast routing attracts more attentions in MANET. Multicast Ad Hoc On-Demand Distance Vector (MAODV) protocol, which is multicast operation of the Ad hoc On-Demand Distance Vector (AODV) routing protocol, enables dynamic, self-starting, multihop routing between participating mobile nodes wishing to join or participate in a multicast group within an Ad Hoc network. Through analyzing its operation mechanism, we present a probability based multicast routing protocol with energy constrained (PBMR),in which a hybrid probability forwarding strategy is adopted. When a node wants to join a multicast tree, it floods a RREQ-Join message to its neighbors, when the neighbors receive this message, the neighbor with much energy left has a high probability to forward the message, and so on. By making correctness proof and complexity analysis of PBMR, this paper evaluates MAODV and PBMR with extensive simulations, the results show that PBMR has a better jitter than MAODV and a more balance in energy consumption. On the one hand, it saves the energy of the network system; on the other hand, it has a longer network lifetime and a more stable routing than MAODV and successfully solves the inconsistent problem of energy and scalability.

Industrial load management plays an important role in the balance of energy consumption and electricity generation, which is increasingly fluctuating due to the growing share of renewable energies. Manufacturing companies are able to adapt their energy consumption by considering energy aspects in their production schedule. It may even be beneficial to temporarily force production resources into idle states and to thus reduce energy demand for a limited period. However, the resulting scheduling problem is very complex and at the same time should be executed in real-time. This paper presents an approach for industrial load management using multi-agent reinforcement learning for energy-oriented rescheduling. A simulation study serves to validate the approach. The results show good solutions and at the same time low computational expense compared to a metaheuristic approach using simulated annealing.