Problems In Internet Of Things Research Articles

Exploring the potential of swarm intelligence for optimal energy efficiency in IoT downlink system

This study delves into the energy optimization problem in Internet of Things (IoT) networks. We consider the downlink from multiple antenna Gateway (GW) and single antenna IoT devices. For this challenging nonconvex problem, we initially introduced the well-known zero-forcing beamforming (ZFBF) to eliminate inter-user interference, thereby transforming the energy efficiency maximization problem into a concave-convex fractional problem. Then, instead of applying a combination of ZFBF with power allocation, we propose the Particle Swarm Optimization (PSO) algorithm to allocate power to find the optimized beamforming matrix. Through extensive numerical analysis, we demonstrate the effectiveness of our proposed scheme in terms of energy efficiency and power achieved at the GW. The results underscore the significant benefits of our approach over conventional methods, paving the way for practical and efficient energy optimization in IoT networks.

Energy and spectrum efficient retransmission scheme with RAW optimization for IEEE 802.11ah networks

Restricted Access Window (RAW) and group-based media access are new features that are exploited by IEEE 802.11ah standard to resolve massive access problem in Internet of Things (IoT) applications. Nevertheless, inefficient device grouping and inappropriate contention resolution in a RAW may still result in collisions and consequently lead to degradation of QoS, energy efficiency, and channel utilization. In existing works, contention resolution schemes schedule all failed devices to retransmit in the next slot of the RAW, which increases the probability of collisions. In this paper, we propose a new retransmission scheme that allows the collided devices of each RAW slot to have another transmission chance in one of the next µ upcoming slots, randomly. We represent the proposed retransmission method via a probabilistic model and formulate a problem based on it, to adjust the number of slots of each RAW with the aim of increasing overall energy efficiency and overall channel utilization regarding delay constraint of devices. Solving the formulated problem, a meta-heuristic algorithm is used to adjust the number of RAW slots and so the size of each RAW regarding the results of the device grouping algorithm. To better exploit the capabilities of the proposed idea, we suggest a load-aware and distance-based device grouping algorithm that not only considers the hidden node problem, but also attends to the load balance of the groups. Simulation results show that the proposed retransmission and RAW adjustment scheme alongside the proposed device grouping algorithm, improve energy efficiency and channel utilization by 25 % and 17 % respectively, and reduce the access delay by 31 % in average, compared to the previous retransmission method.

An Efficient Secure Group Authenticated Key Agreement Protocol for Wireless Sensor Networks in IoT Environment

Internet of Things(IoT) consist of interconnected devices for transmitting and receiving the data over the network. Key management is important for data confidentiality while transmitting in an open network. Even though several key management techniques are feasible to use, still obtaining a key management technique is a challenge with respect to energy and computational cost. The main intention of this work is to discover and overcome the design issues of the existing system and implement a lightweight and secure solution for that issue. The existing system has a fatal security flaw that leads to the unavailability of a complete system which is considered a huge problem in Internet of things. To overcome this issue, an authenticated key management protocol is proposed which deals with the problem of single point of failure and maintains the security properties of the existing system. An authenticated scheme is provided using elliptic curve and hash functions. This scheme also provides client addition, deletion and key freshness. Security analysis and computation complexity has been also discussed. We experimented proposed algorithm and tested with Scyther verification tool. The design overcomes the issues of an existing system by utilizing our scheme in peer to peer network. This network resolves the issue of a single point of failure (SPOF) by distributing the resources and services to the multiple nodes in the network. It will dissolve the problem of SPOF and will increase the reliability and scalability of the IoT system.

CODAS extension using novel decomposed Pythagorean fuzzy sets: Strategy selection for IOT based sustainable supply chain system

Decomposed fuzzy sets (DFSs), which have been recently introduced in the field of fuzzy sets are a fuzzy set extension that deals with the membership degree and the non-membership degree of the elements in the set from a functional and dysfunctional point of view in more detail. On the other hand, Pythagorean fuzzy sets (PFSs) are another fuzzy set extension reflecting the hesitancy of decision-makers in a wider area. The main target of this study is to develop a new fuzzy set extension, decomposed Pythagorean fuzzy sets (DPFSs) in order to utilize the properties of DFSs and PFSs together. First, the basic principles and mathematical operators of the DPFSs are developed with their proofs. Then, a new fuzzy multi-criteria decision-making (MCDM) method, DPF- Combinative Distance based Assessment (CODAS) is developed to demonstrate the feasibility and effectiveness of the DPF extension. The developed method is handled in the strategy selection problem for Internet of Things (IoT)-based sustainable supply chain systems. A comprehensive sensitivity analysis is performed on both the criteria weights and the decision makers' weights to verify the stability and effectiveness of the obtained results. In addition, a comparative analysis with decomposed fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Pythagorean fuzzy CODAS and Pythagorean fuzzy TOPSIS methods is presented to show the validity, superiority and advantages of the developed method.

An Enhanced Flower Pollination Algorithm with Gaussian Perturbation for Node Location of a WSN.

Localization is one of the essential problems in internet of things (IoT) and wireless sensor network (WSN) applications. However, most traditional range-free localization algorithms cannot fulfill the practical demand for high localization accuracy. Therefore, a localization algorithm based on an enhanced flower pollination algorithm (FPA) with Gaussian perturbation (EFPA-G) and the DV-Hop method is proposed.FPA is widely applied, but premature convergence still cannot be avoided. How to balance its global exploration and local exploitation capabilities still remains an outstanding problem. Therefore, the following improvement schemes are introduced. A search strategy based on Gaussian perturbation is proposed to solve the imbalance between the global exploration and local exploitation search capabilities. Meanwhile, to fully exploit the variability of population information, an enhanced strategy is proposed based on optimal individual and Lévy flight. Finally, in the experiments with 26 benchmark functions and WSN simulations, the former verifies that the proposed algorithm outperforms other state-of-the-art algorithms in terms of convergence and search capability. In the simulation experiment, the best value for the normalized mean squared error obtained by the most advanced algorithm, RACS, is 20.2650%, and the best value for the mean distance error is 5.07E+00. However, EFPA-G reached 19.5182% and 4.88E+00, respectively. It is superior to existing algorithms in terms of positioning, accuracy, and robustness.

Weibull Distributive Feature Scaling Multivariate Censored Extreme Learning Classification for Malicious IoT Network Traffic Detection

Malicious Network Traffic identification is the major problems in Internet of Things (IoT) infrastructure. From fast growth and popularization of IoT, threats and attacks in these networks are also increasing correspondingly. To detect Malicious Network Traffic on IoT surroundings, accurate scheme is significant to reduce protection risks on IoT. In this manuscript, Weibull distributive feature scaling multivariate censored extreme learning classification is proposed for Malicious IoT Network Traffic Detection (WDGMS- MCP-ELM-AD-IoT). Initially, the input datas are taken from UNSW-NB15 dataset. The datas are fed to pre-processing. During pre-processing, the redundancy eradication together with replacement of missing value is done for removing uncertainties based on local least squares. The pre-processed data is given to feature selection for choosing relevant features. The process of feature scaling is carried out by Weibull distributive generalized multidimensional scaling (WDGMS). Classification is done with the help of Multivariate Censored phi correlative Extreme learning machines classifier (MCP-ELM) depending on optimum features, which classifies normal or anomalous IoT Network Traffic. Among these, anomalous IoT Network Traffic involves fuzzers, analysis, backdoors, DoS, exploits, generic, reconnaissance, shell code, worm attacks. The proposed approach is activated in Python. The performance metrics, such as accuracy, F-Measure, precision specificity, sensitivity, and false positive rate is evaluated. The simulation of the proposed Weibull distributive feature scaling multivariate censored extreme learning classification for attack detection in IoT (WDGMS- MCP-ELM-AD-IoT) achieves better accuracy 39.45%, 34.58%, 28.29, better sensitivity 26.34%, 21.23%, 27.25%, better specificity 33.67%, 22.37%, 24.78% analyzed to the existing XGBoost-AD-IoT, DNN-AD-IoT and DRaNN-AD-IoT models respectively.

TABI: Trust-Based ABAC Mechanism for Edge-IoT Using Blockchain Technology

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, implementing blockchain technology directly on IoT networks is prone to high overheads and energy-expensive operations. Therefore, in this paper, we use edge computing technology to avoid these problems. We also propose a novel Trust-based Access Control Mechanism for Edge-IoT Networks using Blockchain technology (named TABI) to implement end-to-end security in resource-constrained IoT networks. The TABI mechanism utilizes both access control and trust evaluation mechanisms to mitigate the impact of malicious IoT users and devices. Additionally, it incorporates permissioned Hyperledger blockchain technology to provide an added layer of security through authentication. The trust evaluation mechanism is implemented as a trust calculation contract (TCC) on the edge devices using Hyperledger Composer. The access control mechanism employs an Attribute-based Access Control (ABAC) mechanism, which is implemented on the Hyperledger blockchain using two smart contracts: the attribute contract (AC) and the access control contract (ACC). We implement a proof-of-concept (PoC) implementation using Hyperledger Caliper (a benchmark testing tool) and Docker images. Our evaluation includes five analyses: Trust Evaluation Mechanism, Access Control Mechanism, Security, Blockchain, and IoT Applications. Through this evaluation, we highlight the effectiveness of TABI in terms of throughput, latency, detection of malicious IoT devices, and resource consumption of the IoT devices. Our analyses demonstrate that TABI is particularly useful in IoT applications that require low latency and resource efficiency.

Availability-Aware Multiobjective Task Allocation Algorithm for Internet of Things Networks

Node failures are known to be among the generic problems in Internet of Things (IoT) networks. These failures can be caused by communication disturbances, battery depletion, or even hardware faults. The larger the IoT network and the larger the task to be executed in the network, the higher is the probability of a node failure in the relevant part of the network. This article studies the node failures and proposes a new task allocation algorithm based on multiobjective optimization to address this issue. This article proposes a specialized archive-selection mechanism to enhance diversity in the search space of the so-called multiobjective task allocation algorithm (MOTA). High diversity in the archive allows a reliable selection of alternative task assignments in the case of node failures in the IoT network. We evaluate the performance of the proposed approach regarding the network lifetime, its latency, and its availability, using a network simulation model and compare the results with the baseline MOTA and the dynamic task allocation scheduler (DTAS). The results show that the proposed approach provides significant performance improvements over the existing algorithms, especially in scenarios with high task-to-node ratios.

A Pigeon Group Decomposition Based Algorithm for Logistics Service Transaction Matching Under Blockchain

The transmission security problem in the industrial Internet of Things is the focus of industrial manufacturing development. Blockchain technology can well solve this problem. The massive data generated in the Industrial Internet of Things will be transmitted to the blockchain system through the gateway, the throughput and deployment costs need to be considered. This paper proposes a pigeon flock algorithm based on decomposition to solve this problem. It uses the excellent navigation ability of carrier pigeons to improve search efficiency. At the same time, two independent archives are used to balance convergence and diversity. After the solution set in the convergence archives is classified, the solution set is evaluated by the Tchebycheff function, and some better solutions are retained. The diversity archive chooses some solutions that are quite different from other solutions to maintain diversity. This article uses this algorithm to obtain the solutions with low throughput and high deployment when the blockchain is implemented in the Industrial Internet of Things. Finally, 7 questions in the DTLZ test question are selected to prove the validity of the proposed algorithm. These questions have 5, 10, and 15 goals. In the experimental results, it can be seen that the proposed algorithm is better than NSGAIII, MOEADD and NSGAII on most of the test problems.

Secure Outsourcing of Large-Scale Convex Optimization Problem in Internet of Things

With the development of cloud computing and the advent of Internet of Things(IoT), outsourcing computation, as an important application of cloud computing, has been widely researched in the field of academic and industry. The convex optimization problem, as a most common mathematical problem, often appears in some machine learning algorithms and smart grid designs. However, the process of solving the convex optimization problem is very complicated and time-consuming. For some resource-constrained IoT devices, there are no enough computation resources and storage resources to deal with this problem. In this paper, we proposed an efficient and secure outsourcing algorithm for solving the large-scale convex optimization problem with equality constraints in IoT. Our proposed algorithm can not only reduce the computational complexity on the client side, but also protect the client’s sensitive data from being disclosed to the dishonest cloud server. In addition, the client can detect the malicious behavior from the cloud server with probability approximately 1. Finally, we give a theoretical analysis about correctness and security, and conduct experiments to show the feasibility of our proposed algorithm.

A family system based evolutionary algorithm for obstacle-evasion minimal exposure path problem in Internet of Things

Barrier coverage in wireless sensor networks (WSNs) is a well-known model for military security applications in IoTs, in which sensors are deployed to detect every movement over the predefined border. The fundamental sub-problem of barrier coverage in WSNs is the minimal exposure path (MEP) problem. The MEP refers to the worst-case coverage path where an intruder can move through the sensing field with the lowest capability to be detected. Knowledge about MEP is useful for network designers to identify the worst coverage in WSNs. Most prior research focused on this problem with the assumption that the WSN has an ideal deployment environment without obstacles, causing existing gaps between theoretical and practical WSNs systems. To overcome this drawback, we investigate a systematic and generic MEP problem under real-world environment networks by presenting obstacles called Obstacle-Evasion-MEP (hereinafter OE-MEP). We propose an algorithm to create several types of arbitrary-shaped obstacles inside the deployment area of WSNs. The OE-MEP problem is an NP-Hard with high dimension, non-differentiation, non-linearity, and constraints. Based upon its characteristics, we then devise an elite algorithm namely Family System based Evolutionary Algorithm (FEA) with our newly-proposed concepts of Family System, tailored to efficiently solve the OE-MEP. We also build an extension to a custom-made simulation environment to integrate a variety of network topologies as well as obstacles. Experimental results on numerous instances indicate that the proposed algorithm is suitable for the converted OE-MEP problem and performs better in solution accuracy than existing approaches.

Loopless Variance Reduced Stochastic ADMM for Equality Constrained Problems in IoT Applications

The alternating direction method of multipliers (ADMMs) is an efficient optimization method for solving equality constrained problems in Internet of Things (IoT) applications. Recently, several stochastic variance reduced ADMM algorithms (e.g., SVRG-ADMM) have made exciting progress, such as linear convergence for strongly convex (SC) problems. However, SVRG-ADMM and its variants have an outer loop where the full gradient at the snapshot is computed, and their outer loop contains an inner loop, in which a large number of variance reduced gradients are estimated from random samples. This loopy design makes these methods more complex to analyze and determine the inner loop length, which must be proportional to the condition number to achieve best convergence, and is often set to <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(n)$ </tex-math></inline-formula> as a suboptimal choice, where <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the number of samples. To tackle these issues, we propose an efficient loopless variance reduced stochastic ADMM algorithm, called LVR-SADMM. In our LVR-SADMM, we remove the outer loop and replace it with a biased coin-flip, in which we update the snapshot with a small probability to trigger the full gradient computation. Moreover, we also theoretically analyze the convergence property of LVR-SADMM, which shows that it enjoys a fast linear convergence rate for SC problems. In particular, we also present an accelerated loopless SVRG-ADMM (LAVR-SADMM) method for both SC and non-SC problems. Various experimental results on many real-world data sets verify that the proposed methods can achieve an average speedup of <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> in the SC case and <inline-formula> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula> in the non-SC case over their loopy counterparts, respectively.

Intelligent Technique Based on Enhanced Metaheuristic for Optimization Problem in Internet of Things and Wireless Sensor Network

For the last decade, there has been an intensive research development in the area of wireless sensor networks (WSN). This is mainly due to their growing interest in several applications of the Internet of Things (IoT). Several issues are thus discussed such as node localization, a capability that is highly desirable for performance evaluation in monitoring applications. The localization aim is to look for precise geographical positions of sensors. Recently, swarm intelligence techniques are suggested to deal with localization challenge and localization is seen as an optimization problem. In this article, an Enhanced Fruit Fly Optimization Algorithm (EFFOA) is proposed to solve the localization. EFFOA has a strong capacity to calculate the position of the unknown nodes and converges iteratively to the best solution. Distributing and exploiting nodes is a chief challenge to testing the scalability performance. the EFFOA is simulated under variant studies and scenarios. in addition, a comparative experimental study proves that EFFOA outperforms some of the well-known optimization algorithms.

Computation Offloading and Resource Allocation for the Internet of Things in Energy-Constrained MEC-Enabled HetNets

In this paper, we present our investigation of a latency-minimization offloading problem for internet of things (IoT) terminals in multiple access edge computing (MEC)-enabled heterogeneous networks (HetNets), which jointly optimizes computation and communication resource allocation. Different from related works, the inter-user interferences caused by computation offloading demonstrate effective management in this paper. We also consider the limited battery capacity for IoT terminals for an energy-limited network. Then, we formulate a joint computation offloading and resource allocation optimization problem to minimize the weight-sum delay of users under the constraint of inter-user interference and energy consumption. Since the problem we formulated is a mixed integer non-linear programming (MINLP) problem, the optimal solution can't be easily obtained. Thus, we decompose the problem into multiple sub-problems. First, we obtain the optimal close solution for local CPU frequencies for each user. Then we propose a low complexity algorithm by using the CVX tool and the successive convex approximation approach (SCA). Finally, we propose a distributed computation offloading algorithm. The simulation results compare the performance of the proposed offloading scheme with different algorithms. We also analyze the influence of network parameters on the network latency and obtain some interesting conclusions.

GROLL: Geographic Routing for Low Power and Lossy IoT Networks

Routing in low-power lossy networks (LLNs) remains an important research problem for Internet of Things (IoT), despite the recent standardization in RFC 6550 of RPL as an IPv6 Routing Protocol [1]. Recent deployment experiences [2] have emphasized the challenges faced by a complex design in real world deployments. One of the key challenges is versatile routing protocol design that can handle efficiently and seamlessly various combinations of routing primitives, namely unicast, multicast, and convergecast. Although RPL supports these traffic types, due to its design principle oriented around data collection, its unicast and multicast functionalities are limited. In this article, we present the design and system implementation of the first Geographic Routing framework for Low Power and Lossy Networks (GROLL). GROLL’s routing engine effectively integrates routing for unicast, multicast, and convergecast and takes into account Complex Network Topologies (CNT), i.e., network holes and cuts. GROLL detects CNTs and abstracts the boundary information of detected CNTs with significantly reduced memory size. The proposed unified routing framework was implemented on real hardware and it was evaluated extensively on a testbed of 42 TelosB motes.

Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things.

Energy conservation is one of the most critical problems in Internet of Things (IoT). It can be achieved in several ways, one of which is to select the optimal route for data transfer. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for IoT. The RPL changes its path frequently while transmitting the data from source to the destination, due to high data traffic in dense networks. Hence, it creates data traffic across the nodes in the networks. To solve this issue, we propose Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) for IoT. It has two processes, namely parent selection and data aggregation. The process of parent selection uses routing metric residual energy (RER) to choose the best possible parent for data transmission. The data aggregation process uses the compressed sensing (CS) theory in the parent node to combine data packets from the child nodes. Finally, the aggregated data transmits from a downward parent to the sink. The sink node collects all the aggregated data and it performs the reconstruction operation to get the original data of the participant node. The simulation is carried out using the Contiki COOJA simulator. EDADA-RPL’s performance is compared to RPL and LA-RPL. The EDADA-RPL offers good performance in terms of network lifetime, delay, and packet delivery ratio.

Distributed Conditional Gradient Online Learning for IoT Optimization

Many problems in Internet of Things (IoT) can be cast as distributed optimization problems. For this reason, this paper considers a distributed online constrained optimization problem in IoT, where the local objective functions change with time. In order to solve this problem, distributed projected gradient descent methods are employed frequently. However, the computation of the projection operators is prohibitive in high-dimensional constrained optimization problem. To address the issue, we propose a distributed online learning algorithm based on the conditional gradient method over IoT systems, which avoids the costly projection steps. Moreover, when the local objective functions are strongly convex, we show that the regret bound of O(T) is achieved, where T is a time horizon. When the local objective functions are potentially non-convex, we also show that the algorithm converges to some stationary points at rate of O(T). In addition, we present simulation experiments to confirm the theoretical results.

An Effective Order-Aware Hybrid Genetic Algorithm for Capacitated Vehicle Routing Problems in Internet of Things

The Internet of Things integrates lots of capacitated vehicles in smart logistics. The routing for capacitated vehicles is a combinatorial optimization problem which has been widely studied in recent years. This paper proposes an effective order-aware hybrid genetic algorithm for the capacitated vehicle routing problem in the Internet of Things. The method is characterized by an improved initialization strategy and a problem-specific crossover operator. The former combines the sweep algorithm with randomness to harmonize the contradiction between diversity and convergence, while the latter integrates neighborhood search heuristics to find the offspring with the best fitness and check constraints simultaneously. A large number of simulations have been carried out, and the results validated the effectiveness of our algorithm.

DualFog-IoT: Additional Fog Layer for Solving Blockchain Integration Problem in Internet of Things

Integration of blockchain and Internet of Things (IoT) to build a secure, trusted and robust communication technology is currently of great interest for research communities and industries. But challenge is to identify the appropriate position of blockchain in current settings of IoT with minimal consequences. In this article we propose a blockchain-based DualFog-IoT architecture with three configuration filter of incoming requests at access level, namely: Real Time, Non-Real Time, and Delay Tolerant Blockchain applications. The DualFog-IoT segregate the Fog layer into two: Fog Cloud Cluster and Fog Mining Cluster. Fog Cloud Cluster and the main cloud datacenter work in a tandem similar to existing IoT architecture for real-time and non-real-time application requests, while the additional Fog Mining Cluster is dedicated to deal with only Delay Tolerant Blockchain application requests. The proposed DualFog-IoT is compared with existing centralized datacenter based IoT architecture. Along with the inherited features of blockchain, the proposed model decreases system drop rate, and further offload the cloud datacenter with minimal upgradation in existing IoT ecosystem. The reduced computing load from cloud datacenter doesn't only help in saving the capital and operational expenses, but it is also a huge contribution for saving energy resources and minimizing carbon emission in environment. Furthermore, the proposed DualFog-IoT is also being analyzed for optimization of computing resources at cloud level, the results presented shows the feasibility of proposed architecture under various ratios of incoming RT and NRT requests. However, the integration of blockchain has its footprints in terms of latent response for delay tolerant blockchain applications, but real-time and non-real-time requests are gracefully satisfying the service level agreement.

Enabling robust and reliable transmission in Internet of Things with multiple gateways

This paper investigates the robust and reliable transmission problem in Internet of Things (IoT) applications, where multiple gateways are deployed. We discover that the reliable routing path with the best link qualities may not always gain the reliable transmission. The main reason is that the majority of existing routing metrics generally do not consider the working state of gateways. And the gateways may significantly reduce the reliability of data transmission at the last hop when they operate at the variable duty cycles (e.g., due to insufficient energy harvesting from ambiance). Last-hop data loss will lead to the inefficient transmission in all previous hops. To address this issue, we propose a novel routing metric ETD (Expected Transmission Direction, ETD), which efficiently selects a proper set of gateways with improved reliability in variable duty-cycled IoT through estimating the working state of gateways. Based on ETD, we design an efficient opportunistic routing protocol PoR to ensure reliable data transmission. Our simulations demonstrate the superior performance of PoR. It is shown that PoR achieves over 98% packet delivery ratio even in the worst network setting, with effective load balancing among selected gateways.