中继协同的无线物联能量收集和信息传输算法

本文针对两跳无线物联通信系统,对中继节点利用收集的能量采用功率分流法进行能量收集和信息传输算法进行了设计,其中算法设计是基于能效最大化的优化准则,能效函数定义为瞬时吞吐量与硬件电路总功耗的比值,中继节点利用源节点发送的信号进行能量收集,考虑EARTH计划中实际的功率转换效率和硬件电路损耗的功耗因子,推导了中继协同的无线物联系统能量收集和信息传输的最优功率分配方案的解析解,由于优化问题是非凸问题,为了解决该问题,本文利用高信噪比近似法并通过拉格朗日算法和Lambert W函数获得了优化问题的最优解,数值仿真验证了所提方案的正确性和有效性。      

能量收集多跳D2D无线传感网络中的中继选择算法

针对无线功率传输技术的能量收集效率有限造成信噪比下降进而引发通信中断率增加的问题,在能量收集多跳D2D(Device to Device)无线传感网络中,提出一种基于改进K-means聚类的中继选择方法。首先,推导得到能量收集下的信噪比因子,使其作为K-means聚类特征。然后,利用最小欧氏距离原则得到距离聚类中心最近的实际节点的位置。最后,根据距离重排序得到中继节点,形成从源节点到目的节点的通信链路。仿真实验结果表明,相比最短路径算法和随机中继协作方案,所提出的改进算法链路信噪比更大,能够减小通信中断率,具有更好的中继性能。     

基于节点机会角色转换的M2M上行通信策略

针对M2M(Machine-to-Machine,机器通信)网络,终端节点上行通信过程中多网关节点并存的场景,研究了该场景下的一个简化模型:一个终端节点为信源,两个网关节点竞争成为信宿,把接收到信源发送的数据转发至基站。利用角色转换思想,提出了基于本地信道状态信息的节点机会角色转换策略(ORT-L, Opportunistic Role Transition Protocol based on Local Channel State Information),并研究了该场景下协同网络的中断概率和能量效率性能。从蒙特卡罗仿真中,证明了ORT-L角色转换策略仅仅依靠本地信道状态信息的可行性,并在性能相比于传统协同通信网络在可靠性和能量效率上有很大改善。其理论分析值与仿真结果相吻合。     

距离约束分簇策略下无人机群空地组网性能分析

在无人机(UAV)辅助的能量受限低功耗物联网(IoT)节点数据传输场景中,针对传统的Matérn聚类过程(MCP)建模造成的无人机覆盖冗余问题,该文提出一种Matérn集群下的距离约束分簇策略(MCDC).该策略采用带有距离约束的Matérn聚类过程对无人机和地面IoT节点位置进行了建模,实现冗余覆盖的大幅度下降.在此...     

An Efficient Secure Authentication and Key Establishment Scheme for M2M Communication in 6LoWPAN in Unattended Scenarios

Machine-to-machine (M2M) is an important part of Internet of Things (IoT), and is used to describe those technologies applied in wireless communication automatically between mechanics or electronics instruments. With the rapid development and wide application of the Internet of Things, IETF is assigned to design IPv6 over low power wireless personal area network (6LoWPAN). The address of IPv6 is indefinite, which means it can satisfy addressing requirements for M2M. The 6LoWPAN standard has clarified important issues in M2M, but communication security has not been effectively resolved. In this article, we analyzed the existing security protocol for M2M communication in 6LoWPAN. The analysis result shows that the protocol has the defect of data leakage after the node is captured. In addition, the EAKES6Lo protocol is also vulnerable to sinkhole attacks and plaintext-chosen attacks. Based on the above analysis, an M2M communication mutual authentication protocol based on 6LoWPAN in unattended operation is proposed. The protocol establishes a reasonable secret key distribution mechanism and designs an anti-capture attack detection method for unattended nodes to resist attacks, such as replay attacks, sinkhole attacks, plaintext-chosen attacks, and physical capture attacks. Finally, the security of the protocol is proved by BAN.

     

Modeling and analysis of multiple access channel capacity based on hybrid energy storage and energy harvesting

Energy harvesting (EH) has been considered as a promising technology to solve the constrained energy problem in the devices of IoT with its advantages of flexible deployment and sustainable energy supply.For multiple access channel with energy harvesting,a hybrid energy storage structure model composed by super capacitor and battery was proposed for the devices of IoT.According to the peculiarities of medium access channel and energy harvesting system,an optimized energy allocation strategy with exponential-type decline (ETD) was presented,the upper and lower bounds of the average throughput were deduced,in particular,the gap of two bounds was derived to be a constant.The channel capacity was further obtained by utilizing the relationship between the average throughput and the channel capacity.In the simulations,the effect of harvested energy,storage capacity and the number of nodes on the channel capacity were analyzed respectively.Experiment results show that compared with the conventional wireless node with single battery storage,the proposed hybrid energy storage structure can improve the harvested energy value and increase the multiple access channel capacity by using adaptive modulation scheme when transmitting the signals.     

Energy and Latency-Aware Scheduling Under Channel Uncertainties in LTE Networks for Massive IoT

The machine-to-machine (M2M) communication is an enabler technology for internet of things (IoT) that provides communication between machines and devices without human intervention. One of the main challenges in IoT is managing a large number of machine-type communications co-existing with the human to human (H2H) or human type communications. Long term evolution (LTE) and LTE-advanced (LTE-A) technologies due to their inherent characteristics like high capacity and flexibility in data access management are appropriate choices for M2M/IoT systems. In this paper, a two-phase intelligent scheduling mechanism based on interval type-2 fuzzy logic to (1) satisfy QoS requirements, (2) ensure fair resource allocation and (3) control energy level of devices for coexistence of M2M/H2H traffics in LTE-A networks, is presented. The proposed interval type-2 fuzzy Logic mechanism enhances data traffic efficiency by predicting and handling the network uncertainties. The performance of the proposed algorithm is evaluated in terms of various metrics such as delay, throughput, and bandwidth utilization.     

基于合作微分博弈的物联网能量带宽优化模型（英文）

Internet of Things(IoT) refers to an infrastructure which enables the forms of communication and collaboration between people and things,and between things themselves.In order to improve its performance,we present a tradeoff between bandwidth and energy consumption in the IoT in this paper.A service providing model is built to find the relationship between bandwidth and energy consumption using a cooperative differential game model.The game solution is gotten in the condition of grand coalition,feedback Nash equilibrium and intermediate coalitions and an allocation policy is obtain by Shapley theory.The results are shown as follows.Firstly,the performance of IoT decreases with the increasing of bandwidth cost or with the decreasing of energy cost;secondly,all the nodes in the IoT composing a grand coalition can save bandwidth and energy consumption;thirdly,when the factors of bandwidth cost and energy cost are equal,the obtained number of provided services is an optimised value which is the trade-off between energy and bandwidth consumption.     

A low power high speed MTJ based non-volatile SRAM cell for energy harvesting based IoT applications

Powering billions of devices is one of the most challenging barrier in achieving the future vision of IoT. Most of the sensor nodes for IoT based systems depend on battery as their power source and therefore fail to meet the design goals of lifetime power supply, cost, reliable sensing and transmission. Energy harvesting has the potential to supplant batteries and thus prevents frequent battery replacement. However, energy autonomous systems suffer from sudden power variations due to change in external natural sources and results in loss of data. The memory system is a main component which can improve or decrease performance dramatically. The latest versions of many computing system use chip multiprocessor (CMP) with on-chip cache memory organized as array of SRAM cell. In this paper, we outline the challenges involved with the efficient power supply causing power outage in energy autonomous/self-powered systems. Also, various techniques both at circuit level and system level are discussed which ensures reliable operation of IoT device during power failure. We review the emerging non-volatile memories and explore the possibility of integrating STT-MTJ as prospective candidate for low power solution to energy harvesting based IoT applications. An ultra-low power hybrid NV-SRAM cell is designed by integrating MTJ in the conventional 6T SRAM cell. The proposed LP8T2MTJ NV-SRAM cell is then analyzed using multiple key performance parameters including read/write energies, backup/restore energies, access times and noise margins. The proposed LP8T2MTJ cell is compared to conventional 6T SRAM counterpart indicating similar read and write performance. Also, comparison with the existing MTJ based NV-SRAM cells show 51–78% reduction in backup energy and 42–70% reduction in restore energy.     

Energy‐efficient resource allocation for lifetime maximization in M2M networks

Machine‐to‐machine (M2M) communications is one of the major enabling technologies for the realization of the Internet of Things (IoT). Most machine‐type communication devices (MTCDs) are battery powered, and the battery lifetime of these devices significantly affects the overall performance of the network and the quality of service (QoS) of the M2M applications. This paper proposes a lifetime‐aware resource allocation algorithm as a convex optimization problem for M2M communications in the uplink of a single carrier frequency division multiple access (SC‐FDMA)‐based heterogeneous network. A K‐means clustering is introduced to reduce energy consumption in the network and mitigate interference from MTCDs in neighbouring clusters. The maximum number of clusters is determined using the elbow method. The lifetime maximization problem is formulated as a joint power and resource block maximization problem, which is then solved using Lagrangian dual method. Finally, numerical simulations in MATLAB are performed to evaluate the performance of the proposed algorithm, and the results are compared to existing heuristic algorithm and inbuilt MATLAB optimal algorithm. The simulation results show that the proposed algorithm outperforms the heuristic algorithm and closely model the optimal algorithm with an acceptable level of complexity. The proposed algorithm offers significant improvements in the energy efficiency and network lifetime, as well as a faster convergence and lower computational complexity.     

多标签无线供电反向散射通信网络能效优化算法

为了提高物联网(IoT)节点的运行周期和能量利用率,该文提出一种多标签无线供电反向散射通信网络能效最大化资源分配算法。考虑传输速率约束、能量收集约束以及发射功率约束,建立了基于系统能效最大化的资源分配模型。利用Dinkelbach理论、2次变换以及变量替换法,将原分式非凸问题转化为可求解的凸优化问题。通过拉格朗日对偶理论求得优化问题的全局最优解。仿真结果表明,该算法具有较好的收敛性和能效。     

Energy and load balancing routing protocol for IoT

Due to the limitation of node energy resources, the management of energy consumption is one of the most important problems of the internet of things (IoT). Therefore, many studies have tried to optimize and manage energy consumption by focusing on different techniques. Although each of these studies has improved and optimized energy consumption, there are many important problems, including maintaining traffic balance and energy consumption of network nodes. Therefore, a new method is necessary to maintain the load and energy balancing of network nodes. Therefore, this paper introduces energy and load balancing routing protocol for IoT (ELBRP) based on the development of the RPL routing protocol and the efficiency of data distribution technique. The ELBRP performance has three steps. In the first step, along with the process of sending DODAG information object (DIO) messages, the status of network nodes is evaluated. In the second step, the DODAG communication graph is formed according to the ELBRP. In the third step, data transmission is done according to the distribution technique with the goal of balancing traffic and energy. The simulation results using cooja simulator showed the superiority of ELBRP in improving energy consumption and successful delivery ratio, reducing delay and increasing the network lifetime compared to the similar methods.     

Age of information analysis for internet of things information status update system with cellular backhaul and dedicated energy harvesting beacons

The Internet of Things (IoT) that is usually deployed with the assistance of cellular backhaul and energy harvesting (EH) is characterized by the status update freshness. However, the traditional nature EH results in the loss of information freshness due to the randomness of nature energy process. Although using wireless energy transferring (WET) to charge sensors can overcome this problem, this method has low-energy efficiency. With these considerations, this paper proposes a novel IoT system under cellular communication scenario by simultaneously exploiting the nature EH and WET. The proposed IoT system is consisted of a cellular backhaul subsystem, a wireless EH and transferring (WEHT) subsystem, and a wireless sensor status update subsystem. The WEHT subsystem employs the dedicated power beacons harvesting energy from natural energy source and transferring the harvested energy to sensor. For the proposed IoT system, this paper first investigates the WEHT subsystem and constructs the Markov Chain (MC) of discrete energy states as well as the MC state transition matrix. Second, the average AoI and peak AoI (PAoI) are formulated by separately considering cellular backhaul and EHTBs. The AoI (PAoI) comparison shows that the proposed EHTB-based IoT system can outperform the conventional non-EHTB one where the sensor directly harvests energy from natural source. At the same time, the numerical results exploit the impact of system parameters on AoI and PAoI, respectively. It is found that there exists a trade-off between the nature energy arrival and EHTB energy transfer so that the average minimum AoI and PAoI are achieved.     

CrowWhale-ETR: CrowWhale optimization algorithm for energy and trust aware multicast routing in WSN for IoT applications

WSN serves as a medium for linking the physical and information network of IoT. Energy and trust are the two major factors that facilitate reliable communication in the network. During multicast routing, the BS engages in forwarding the data securely to the multiple destinations through the intermediate nodes, which is the major challenge in IoT. The paper addresses the challenges through proposing an energy-aware multicast routing protocol based on the optimization, CrowWhale-ETR, which is the integration of CSA and WOA based on the objective function designed with the energy and trust factors of the nodes. Initially, the trust and energy of the nodes are evaluated for establishing the routes that is chosen optimally using CWOA. This optimally chosen path is used for the data transmission, in which energy and trusts of the individual nodes are updated at the end of the individual transmission, in such a way the secure nodes can be selected, and which improves the secure communication in the network. The simulation is analyzed using 50 and 100 nodes in terms of the performance measures. The proposed method acquired the minimal delay of 0.2729 and 0.3491, maximal detection rate of 0.6726, maximal energy of 66.4275 and 71.0567, and maximal throughput of 0.4625 and 0.8649 in the presence and absence of attacks with 50 nodes for analysis.

     

Optimal Resource allocation in NOMA‐based M2M communication using Hybrid Rider Optimization with FireFly: Power Saving Strategy of IoT

Nowadays, the Orthogonal Multiple Access (OMA) principle has utilized for allocating proper radio resources in wireless networks. However, as the count of users rises, OMA‐based approaches may not satisfy the stringent emerging requirements including very low latency, very high spectral efficiency, and massive device connectivity. Moreover, there are significant challenges in cellular‐enabled Machine‐to‐Machine (M2M) communications due to the unique features of M2M‐based applications. In order to overwhelm these challenges, non‐orthogonal multiple access (NOMA) principles emerge as a solution to enhance the spectral efficiency while allowing some degree of multiple access interference at receivers. Hence, this paper intends to develop an optimal resource allocation mechanism for M2M communication. Here, the nonlinear energy harvesting performed with the aid of an accessing technology termed as NOMA. The key objective of the proposed resource allocation model is the minimization of the total energy consumption of the network. For attaining the minimized power consumption, the time allocation, and transmission power of NOMA is optimally tuned by a hybrid optimization algorithm. The proposed hybrid algorithm merges the beneficial concepts of Rider Optimization Algorithm (ROA) and FireFly (FF) algorithm and implements a new algorithm termed as FireFly Modified Bypass‐based Rider Optimization Algorithm (FMB‐ROA). Finally, the analysis of total energy concerning various constraints validates the performance of the proposed model over conventional models.     

Optimization of energy-constrained wireless powered communication networks with heterogeneous nodes

In this paper, we generalize conventional time division multiple access (TDMA) wireless networks to a new type of wireless networks coined generalized wireless powered communication networks (g-WPCNs). Our prime objective is to optimize the design of g-WPCNs where nodes are equipped with radio frequency (RF) energy harvesting circuitries along with constant energy supplies. This constitutes an important step towards a generalized optimization framework for more realistic systems, beyond prior studies where nodes are solely powered by the inherently limited RF energy harvesting. Towards this objective, we formulate two optimization problems with different objective functions, namely, maximizing the sum throughput and maximizing the minimum throughput (maxmin) to address fairness. First, we study the sum throughput maximization problem, investigate its complexity and solve it efficiently using an algorithm based on alternating optimization approach. Afterwards, we shift our attention to the maxmin optimization problem to improve the fairness limitations associated with the sum throughput maximization problem. The proposed problem is generalized, compared to prior work, as it seemlessly lends itself to prior formulations in the literature as special cases representing extreme scenarios, namely, conventional TDMA wireless networks (no RF energy harvesting) and standard WPCNs, with only RF energy harvesting nodes. In addition, the generalized formulation encompasses a scenario of practical interest we introduce, namely, WPCNs with two types of nodes (with and without RF energy harvesting capability) where legacy nodes without RF energy harvesting can be utilized to enhance the system sum throughput, even beyond WPCNs with all RF energy harvesting nodes studied earlier in the literature. We establish the convexity of all formulated problems which opens room for efficient solution using standard techniques. Our numerical results show that conventional TDMA wireless networks and WPCNs with only RF energy harvesting nodes are considered as lower bounds on the performance of the generalized problem setting in terms of the maximum sum throughput and maxmin throughput. Moreover, the results reveal valuable insights and throughput-fairness trade-offs unique to our new problem setting.

     

Fuzzy logic–based distributed clustering protocol to improve energy efficiency and stability of wireless smart sensor networks for farmland monitoring systems

Wireless smart sensor networks (WSSNs) are emerging as the physical backbone of the internet of things (IoT) technology. On the basis of the IoT platform, web‐based systems and services are been developing such as e‐surveillance, industrial‐IoT, and precision agriculture. For farmland monitoring systems, WSSNs need to be scalable in terms of coverage area. Sensor nodes are energy‐constrained devices, and hence, many energy‐efficient clustering protocols are developed in the literature. But these methods overload the cluster leaders (CLs) with cluster computation and data communication costs. An improper CL selection may lead to the early death of such nodes and hence does not prolong the network lifetime stability. We propose a fuzzy logic (FL)–based distributed clustering protocol to enhance the energy efficiency of WSSN while maximizing the coverage area. The load of CLs is shared by originators and super‐CLs (SCLs) selected in the network. The wireless link and received signal strength (RSS) are greatly affected by environmental conditions and thus cannot be considered as ideal network parameters. We use FL systems to tackle the uncertainty of such network parameters. The proposed protocol is simulated for different scalable WSSNs. The results indicate that the proposed protocol provides better lifetime stability than the recent conventional protocols. The functionalities of the protocol are proposed considering the recent wireless standards. Hence, the proposed protocol can be suitably implemented for farmland monitoring systems.     

The importance of nature-inspired metaheuristic algorithms in the data routing and path finding problem in the internet of things

Over the last decade, the Internet of Things (IoT) has become ever more popular, as is evident from its role in changing the human lifestyle and conferring remarkable privileges for them. It has a significant presence in various crucial areas, including smart cities, smart factories, manufacturing, transportation, and healthcare. Massive amounts of data generated by IoT devices have the potential to endanger the lifetime of nodes in IoT-based networks due to increased communication power consumption. It has become crucial to propose solutions for network-based issues, such as quality of service, security, network heterogeneity, congestion avoidance, reliable routing, and energy conservation. To address the mentioned problems, routing protocols play a critical role in data transmission among heterogeneous items. In such environments, routing refers to constructing routes between mobile nodes. Since identifying optimal routes among IoT nodes and establishing an effective routing protocol in an IoT network are an NP-hard issue, employing metaheuristic algorithms may be a viable solution to overcome this problem. Various IoT routing protocols based on metaheuristic algorithms have been presented in recent years, but there is still a lack of systematic study for reviewing the existing works. The current study emphasizes the impact of metaheuristic algorithms in the IoT routing problem, discusses the optimization models, presents a comprehensive comparison of protocols based on critical parameters, and eventually suggests some hints for future studies.     

Energy Efficient and Reliable ARQ Scheme (E^2R-ACK) for Mission Critical M2M/IoT Services

Wireless sensor networks (WSNs) are the main infrastructure for machine to machine (M2M) and Internet of thing (IoT). Since various sophisticated M2M/IoT services have their own quality-of-service (QoS) requirements, reliable data transmission in WSNs is becoming more important. However, WSNs have strict constraints on resources due to the crowded wireless frequency, which results in high collision probability. Therefore a more efficient data delivering scheme that minimizes both the transmission delay and energy consumption is required. This paper proposes energy efficient and reliable data transmission ARQ scheme, called energy efficient and reliable ACK (E \(^2\) R-ACK), to minimize transmission delay and energy consumption at the same time. The proposed scheme has three aspects of advantages compared to the legacy ARQ schemes such as ACK, NACK and implicit-ACK (I-ACK). It consumes smaller energy than ACK, has smaller transmission delay than NACK, and prevents the duplicated retransmission problem of I-ACK. In addition, resource considered reliability (RCR) is suggested to quantify the improvement of the proposed scheme, and mathematical analysis of the transmission delay and energy consumption are also presented. The simulation results show that the E \(^2\) R-ACK scheme achieves high RCR by significantly reducing transmission delay and energy consumption.     

M2M的业务范畴及其技术发展

指出M2M是使用电信运营商提供的网络和业务支撑平台并提供给其他各种规模垂直行业的公共物联网,它是物联网应用的一个子集。M2M的主要技术包括Thingbased M2M技术、机器对机器直接通信技术和M2M通信网技术。M2M市场潜力巨大,但还存在很多不确定因素,需要我们开展更多研究。