Cyber-Physical Systems Devices Research Articles

Software and Architecture Orchestration for Process Control in Industry 4.0 Enabled by Cyber-Physical Systems Technologies

In the context of Industry 4.0, this paper explores the vital role of advanced technologies, including Cyber–Physical Systems (CPS), Big Data, Internet of Things (IoT), digital twins, and Artificial Intelligence (AI), in enhancing data valorization and management within industries. These technologies are integral to addressing the challenges of producing highly customized products in mass, necessitating the complete digitization and integration of information technology (IT) and operational technology (OT) for flexible and automated manufacturing processes. The paper emphasizes the importance of interoperability through Service-Oriented Architectures (SOA), Manufacturing-as-a-Service (MaaS), and Resource-as-a-Service (RaaS) to achieve seamless integration across systems, which is critical for the Industry 4.0 vision of a fully interconnected, autonomous industry. Furthermore, it discusses the evolution towards Supply Chain 4.0, highlighting the need for Transportation Management Systems (TMS) enhanced by GPS and real-time data for efficient logistics. A guideline for implementing CPS within Industry 4.0 environments is provided, focusing on a case study of real-time data acquisition from logistics vehicles using CPS devices. The study proposes a CPS architecture and a generic platform for asset tracking to address integration challenges efficiently and facilitate the easy incorporation of new components and applications. Preliminary tests indicate the platform’s real-time performance is satisfactory, with negligible delay under test conditions, showcasing its potential for logistics applications and beyond.

Characterizing the Performance of Accelerated Jetson Edge Devices for Training Deep Learning Models

Deep Neural Network (DNN) models are becoming ubiquitous in a variety of contemporary domains such as Autonomous Vehicles, Smart cities and Healthcare. They help drones to navigate, identify suspicious activities from safety cameras, and perform diagnostics over medical imaging. Fast DNN inferencing close to the data source is enabled by a growing class of accelerated edge devices such as NVIDIA Jetson and Google Coral which host low-power Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) along with ARM CPUs in a compact form-factor to offer a superior performance-to-energy ratio. E.g., the NVIDIA Jetson AGX Xavier kit has a 512-core Volta GPU, an 8-core ARM CPU and 32GB LPDDR4x memory, that operates within 65W of power, costs US999 and is smaller than a paperback novel. Recently, there has been a push towards training DNN models on the edge [2]. This is driven by the massive growth in data collected from edge devices in Cyber-Physical Systems (CPS) and Internet of Things (IoT), the need to refresh the models periodically, the bandwidth constraints in moving all this data to Cloud data centers for training, and a heightened emphasis on privacy by retaining data on the edge. This has led to techniques like federated and geo-distributed learning that train DNN models locally on data on an edge device and aggregate them centrally. In this abstract, we summarise and highlight key results from our full paper [5].

Filtering Out High Noise Data for Distributed Deep Neural Networks

Artificial intelligence-based cyber-physical systems (CPS) applications have been spread across various fields such as smart cities, medical services, and industrial controls. When CPS devices are connected to a cloud server, big data streams generated by CPS devices impose enormous bandwidth pressure and exert excessive compute loads to the cloud server. Due to unpredictable environments and uncertainty in reality, these issues are mainly attributed to a large amount of high noise data captured and uploaded by CPS devices. To overcome these issues, this paper proposes a cyber-physical-cloud based framework for distributed deep neural networks (DDNNs) to prevent high noise data from being uploaded to the cloud. The proposed framework features a lightweight data filtering module enabled by depthwise separable convolutions to identify and filter out the high noise data that the cloud cannot recognize. Extensive experimental results demonstrate that the proposed data filtering module can achieve an accuracy of up to 83.72% in identifying high noise data and the proposed framework can effectively save bandwidth of up to 63.42% as compared to benchmarking methods. Note to Practitioners—This paper is motivated by the problems of enormous bandwidth pressure and excessive cloud compute loads in cyber-physical-cloud distributed computing paradigms. These problems are mainly caused by high noise data generated by CPS devices, because CPS devices often work in disturbing and unstable environments and there are uncontrollable uncertainties in reality. Especially for the emerging artificial intelligence-driven cyber-physical-cloud distributed paradigms, there is no existing research to solve the unnecessary transmission and cloud compute loads caused by high noise data. To tackle the challenge, this paper develops a novel cyber-physical-cloud distributed framework with data filtering capabilities to prevent high noise data from being uploaded. The proposed framework supports two popular loosely coupled and closely coupled distributed computing paradigms. Extensive experiments confirm that the proposed cyber-physical-cloud distributed framework can efficiently filter out high noise data and alleviate unnecessary transmission and needless cloud compute loads introduced by high noise data.

Honeynet Implementation in Cyber Security Attack Prevention with Data Monitoring System Using AI Technique and IoT 4G Networks

Cyber Physical Systems (CPS) comprises of the ubiquitous object concept those are connected with Internet to provide ability of data transmission and sensing over network. The smart appliances transmits the data through CPS devices with the implementation of Internet of Things (IoT) exhibits improved performance characteristics with significant advantages such as time savings, reduced cost, higher human comfort and efficient electricity utilization. In the minimal complexity sensor nodes cyber physical system is adopted for the heterogeneous environment for the wireless network connection between clients or hosts. However, the conventional security scheme uses the mechanisms for desktop devices with efficient utilization of resources in the minimal storage space environment, minimal power processing and limited energy backup. This paper proposed a Secure Honeynet key authentication (SHKA) model for security attack prevention through effective data monitoring with IoT 4G communication. The proposed SHKA model uses the lightweight key agreement scheme for authentication to provide security threats and confidentiality issues in CPS applications. With the implementation of SHKA HoneyNet model the data in IoT are monitored for security mechanism in IoT environment. The middleware module in SHKA scheme uses the Raspberry platform to establish internetworking between CPS device to achieve dynamic and scalability. The secure IoT infrastructure comprises of flexible evaluation of user-centric environment evaluation for the effectiveness. The developed SHKA model perform mutual authentication between CPS devices for minimal computation overhead and efficiency. The wireless channel uses the dynamic session key for the secure communication for cyber-attacks security with lightweight security in CPS system. The SHKA model demonstrate the effectiveness based on consideration of three constraints such as low power processing, reduced storage and minimal backup energy. Experimental analysis stated that proposed SHKA scheme provides lightweight end-to-end key establishment in every session. The CPS devices generates the session key of 128 bit long. The minimum key size is implemented to provide effective security in IoT 4G communication with minimal execution time. The simulation results demonstrated that SHKA model exhibits effective cyber-attacks for the constraint devices to improve performance of IoT network.

Privacy‐preserving and fine‐grained data sharing for resource‐constrained healthcare CPS devices

AbstractMedical cyber‐physical systems (CPS) provide the possibility for real‐time health monitoring of patients and flexible diagnostic services based on expert systems by collaboratively integrating and connecting various physical devices including sensors, terminals, and cloud infrastructure. However, the ubiquitous security threats in cyberspace have raised concerns about data security and user privacy. Although related works propose to protect data security and user privacy with cryptographic protocols, their heavy computational and storage overheads incur performance and battery life challenges for resource‐constrained devices in the healthcare CPS. This article proposes an energy‐saving and privacy‐preserving data sharing (ESPPDS) scheme to address the challenge. ESPPDS inherits the anonymous fine‐grained access control from attribute‐based encryption (ABE) while protecting data integrity and supporting efficient user revocation. We also eliminate the repetitive computations of ciphertext components by utilizing the online/ offline encryption technology, and design a subtle and secure trick to delegate the decryption operations to the edge device, thereby reducing the computational overheads of the resource‐constrained devices. We then show the security proof, and discuss the construction in the untrusted/ compromised server setting. The comparison and experiment indicate that ESPPDS is practical and more efficient than related schemes.

Deep Learning for Secure Wireless Communications in Cyber Physical Systems

Deep learning has strong potential using data-driven solutions to improve the performance of cyber physical systems (CPS) in utilizing limited spectrum resources in wireless communications. Deep learning is also a powerful method of data exploration to learn about “normal” and “abnormal” behavior according to how CPS components and devices interact with each other. Furthermore, deep learning methods are important in predicting new attacks, which are often mutations of previous attacks because they can intelligently predict future unknown attacks by learning from existing examples. The topic of deep learning for secure wireless communications in CPS has gained much momentum in the industry and the research community very recently. In this issue of IEEE Wireless Communications, we are pleased to present a Feature Topic on “Deep Learning Driven Secure Communication for Cyber Physical Systems” to bring together researchers, industry practitioners, and individuals working on the related areas to address some of the technical challenges faced by the research topics. This Feature Topic includes a collection of 10 articles chosen by Guest Editors W. Wei, C.-H. Hsu, V. Piuri, and A. Rayes. We are also pleased to present a second Feature Topic on “Enhanced Fronthaul for 5G and Beyond” that includes a collection of four articles chosen by Guest Editors Y. Luo, P. Chanclou, K. E. Skouby, H. Zhao, and K. Asaka. Thanks to the two Guest Editor teams who did an excellent job in editing these two Feature Topics for our readers. Please stay tuned for new developments in these research areas, and read the editorials and articles in the two Feature Topics.

A lightweight device-level Public Key Infrastructure with DRAM based Physical Unclonable Function (PUF) for secure cyber physical systems

Cyber–Physical Systems (CPS) are becoming increasingly ubiquitous and serve as a platform for connecting vast majority of everyday devices to the Internet. Traditional computer-based security systems are not preferred in CPS devices due to limited storage, security issues, and computation power, thus necessitating the demand for efficient and secure computation techniques that would ensure robust CPS platforms. In this paper, we proposed a lightweight Public Key Infrastructure (PKI) scheme that can be used in CPS devices. Conventional PKI systems are characterized with three resource-intensive processes, namely, key generation, certificate validation, and certificate revoking. The proposed method uses Physical Unclonable Function (PUF) to simplify the key generation process and introduces the session key concept to address the other two limitations, viz., certificate validation and revoking. In addition to this, the proposed method addresses some crucial security needs of the PKI scheme, such as node registration, strong random number generation, defining trust relationships among the different “thing” nodes. Additionally, PUF-based trust relationships require reduced infrastructural setup, lesser power, and storage capacity while also incorporating Elliptic Curve Cryptography (ECC) for asymmetric key pair generation. The proposed method has been realized using a Dynamic Random Access Memory (DRAM)-based PUF. The experimental result proves the technique’s efficiency compared to the state-of-the-art methods with a 4–7 times, consuming 5–10 times, lesser energy.

(Retracted) Secure framework against cyber attacks on cyber-physical robotic systems

Robot-based platforms and processes have integrated the security and efficiency of data into a comprehensive range for domains, such as manufacturing, industrial, logistical, agricultural, healthcare, and internet services. Smart cyberattacks have been on the rise, specifically targeting corporate, industrial robotic systems. These attacks execute once the internet of things, internet, and organization integration is implemented with the industrial units. We implemented security criteria-based indices for cyber-physical systems (CPS) with industrial components and embedded sensors that process the information logs and processes. We proposed an attack tree-based secure framework that does not include every CPS device but takes into consideration the critical exploitable vulnerabilities to execute the attacks. We categorized each physical device and integrated sensors based on logs and information in a sensor indices device library. This research simulated real-time exploitation of vulnerabilities on CPS robotic systems using the proposed framework in form of a two-phased process. This validates the enhanced data security output of the integrated sensor and physical nodes with the intelligent monitor and controller system health monitor during real-time cyberattacks This research simulated common cyberattacks on cyber-physical controller servers based on cross-site scripting and telnet pivoting. The authors gathered known and unknown vulnerabilities and exploited them with a tree-based attack algorithm. The authors calculated the average time for cyberattackers with different skills when trying to compromise CPS devices and systems.

SCOPE: Secure Compiling of PLCs in Cyber-Physical Systems

Cyber-Physical Systems (CPS) are being widely adopted in critical infrastructures, such as smart grids, nuclear plants, water systems, transportation systems, manufacturing and healthcare services, among others. However, the increasing prevalence of cyberattacks targeting them raises a growing security concern in the domain. In particular, memory-safety attacks, that exploit memory-safety vulnerabilities, constitute a major attack vector against real-time control devices in CPS. Traditional IT countermeasures against such attacks have limitations when applied to the CPS context: they typically incur in high runtime overheads; which conflicts with real-time constraints in CPS and they often abort the program when an attack is detected, thus harming availability of the system, which in turn can potentially result in damage to the physical world. In this work, we propose to enforce a full-stack memory-safety (covering user-space and kernel-space attack surfaces) based on secure compiling of PLCs to detect memory-safety attacks in CPS. Furthermore, to ensure availability, we enforce a resilient mitigation technique that bypasses illegal memory access instructions at runtime by dynamically instrumenting low-level code. We empirically measure the computational overhead caused by our approach on two experimental settings based on real CPS. The experimental results show that our approach effectively and efficiently detects and mitigates memory-safety attacks in realistic CPS.

Deep-Learning–Based App Sensitive Behavior Surveillance for Android Powered Cyber–Physical Systems

Android as an operating system is now increasingly being adopted in industrial information systems, especially with cyber-physical systems (CPS). This also puts Android devices onto the front line of handling security-related data and conducting sensitive behaviors, which could be misused by the increasing number of polymorphic and metamorphic malicious applications targeting the platform. The existence of such malware threats, therefore, call for more accurate identification and surveillance of sensitive Android app behaviors, which is essential to the security of CPS and Internet of Things (IoT) devices powered by Android. Nevertheless, achieving dynamic app behavior monitoring and identification on real CPS powered by Android is challenging because of restrictions from the security and privacy model of the platform. In this article, the authors investigate how the latest advances in deep learning could address this security problem with better accuracy. Specifically, a deep learning engine is proposed that detects sensitive app behaviors by classifying patterns of system-wide statistics, such as available storage space and transmitted packet volume, using a customized deep neural network based on existing models called Encoder and ResNet. Meanwhile, to handle resource limitations on typical CPS and IoT devices, sparse learning is adopted to reduce the amount of valid parameters in the trained neural network. Evaluations show that the proposed model outperforms a well-established group of baselines on time series classification in identifying sensitive app behaviors with background noise and the targeted behaviors potentially overlapping.

CPS Device-Class Identification via Behavioral Fingerprinting: From Theory to Practice

Cyber-Physical Systems (CPS) utilize different devices to collect sensitive data, communicate with other systems, and monitor essential processes in critical infrastructure applications. However, in the ecosystem of CPS, unauthorized or spoofed devices may danger or compromise the performance and security of the critical infrastructure. The unauthorized and spoofed devices may include tampered pieces of software or hardware components that can negatively impact CPS operations or collect vital CPS metrics from the network. Such devices can be outsider or insider threats trying to impersonate other real CPS devices via spoofing their legitimate identifications to gain access to systems, steal information, or spread malware. Device fingerprinting techniques are promising approaches to identify unauthorized or illegitimate devices. However, current fingerprinting solutions are not suitable as they disrupt critical real-time operations in CPS due to the nature of their extensive data analysis or too much overhead on the devices' computational resources. To address these concerns, in this work, we propose STOP-AND- FRISK (S&F), a novel fingerprinting framework to identify CPS device classes and complement traditional security mechanisms in CPS. S&F is based on a secure challenge/response mechanism that analyzes the behavior of the CPS devices at both the hardware and OS/kernel levels. Specifically, the proposed novel mechanism combines system and function call tracing techniques, signal processing, and hardware performance analysis to create specific device-class signatures. Then, the signatures are correlated against known behavioral ground-truth to identify the device types. To test the efficacy of S&F extensively, we implemented a realistic testbed that included different classes of CPS devices with a variety of computing resources, architectures, and configurations. Our experimental results reveal an excellent rate on the CPS device-class identification. Finally, extensive performance analysis demonstrates that the use of S&F yields minimal overhead on the CPS devices' computing resources.

A Minimum Defense Cost Calculation Method for Attack Defense Trees

The cyberphysical system (CPS) is becoming the infrastructure of society. Unfortunately, the CPS is vulnerable to cyberattacks, which may cause environmental pollution, property losses, and even casualties. Furthermore, in contrast to the conventional Internet, the devices in CPSs are more specific, and the device systems may not be upgraded or installed with new programs during their life spans. The selection of the best defense nodes for defeating cyberattacks is quite challenging in CPSs. To overcome this issue, several attack-defense modeled methods have been proposed. However, few existing studies have considered the defense cost, which is usually a determinant in practice. In this paper, we propose a method for choosing optimal defense nodes that (1) can defeat specific attacks and (2) are inexpensive. First, the atom attack defense tree (A2DTree) is proposed by adding constraints to the conventional attack defense tree (ADTree). Second, the algebraic method is used to efficiently calculate the minimum defense cost. On this basis, a minimum defense cost calculation tool is designed and implemented. Finally, the effectiveness of the proposed method is verified with two typical case studies, and a comparative experiment of related work is carried out. The results show that the method can correctly and efficiently identify the optimal defense nodes and calculate the minimum defense cost of a CPS.

A Multilabel Fuzzy Relevance Clustering System for Malware Attack Attribution in the Edge Layer of Cyber-Physical Networks

The rapid increase in the number of malicious programs has made malware forensics a daunting task and caused users’ systems to become in danger. Timely identification of malware characteristics including its origin and the malware sample family would significantly limit the potential damage of malware. This is a more profound risk in Cyber-Physical Systems (CPSs), where a malware attack may cause significant physical damage to the infrastructure. Due to limited on-device available memory and processing power in CPS devices, most of the efforts for protecting CPS networks are focused on the edge layer, where the majority of security mechanisms are deployed. Since the majority of advanced and sophisticated malware programs are combining features from different families, these malicious programs are not similar enough to any existing malware family and easily evade binary classifier detection. Therefore, in this article, we propose a novel multilabel fuzzy clustering system for malware attack attribution. Our system is deployed on the edge layer to provide insight into applicable malware threats to the CPS network. We leverage static analysis by utilizing Opcode frequencies as the feature space to classify malware families. We observed that a multilabel classifier does not classify a part of samples. We named this problem the instance coverage problem. To overcome this problem, we developed an ensemble-based multilabel fuzzy classification method to suggest the relevance of a malware instance to the stricken families. This classifier identified samples of VirusShare, RansomwareTracker, and BIG2015 with an accuracy of 94.66%, 94.26%, and 97.56%, respectively.

A System-level Behavioral Detection Framework for Compromised CPS Devices

Cyber-Physical Systems (CPS) play a significant role in our critical infrastructure networks from power-distribution to utility networks. The emerging smart-grid concept is a compelling critical CPS infrastructure that relies on two-way communications between smart devices to increase efficiency, enhance reliability, and reduce costs. However, compromised devices in the smart grid poses several security challenges. Consequences of propagating fake data or stealing sensitive smart grid information via compromised devices are costly. Hence, early behavioral detection of compromised devices is critical for protecting the smart grid’s components and data. To address these concerns, in this article, we introduce a novel and configurable system-level framework to identify compromised smart grid devices. The framework combines system and function call tracing techniques with signal processing and statistical analysis to detect compromised devices based on their behavioral characteristics. We measure the efficacy of our framework with a realistic smart grid substation testbed that includes both resource-limited and resource-rich devices. In total, using our framework, we analyze six different types of compromised device scenarios with different resources and attack payloads. To the best of our knowledge, the proposed framework is the first in detecting compromised CPS smart grid devices with system and function-level call tracing techniques. The experimental results reveal an excellent rate for the detection of compromised devices. Specifically, performance metrics include accuracy values between 95% and 99% for the different attack scenarios. Finally, the performance analysis demonstrates that the use of the proposed framework has minimal overhead on the smart grid devices’ computing resources.

Energy-efficient Static Task Scheduling on VFI-based NoC-HMPSoCs for Intelligent Edge Devices in Cyber-physical Systems

The interlinked processing units in modern Cyber-Physical Systems (CPS) creates a large network of connected computing embedded systems. Network-on-Chip (NoC)-based Multiprocessor System-on-Chip (MPSoC) architecture is becoming a de facto computing platform for real-time applications due to its higher performance and Quality-of-Service (QoS). The number of processors has increased significantly on the multiprocessor systems in CPS; therefore, Voltage Frequency Island (VFI) has been recently adopted for effective energy management mechanism in the large-scale multiprocessor chip designs. In this article, we investigated energy-efficient and contention-aware static scheduling for tasks with precedence and deadline constraints on intelligent edge devices deploying heterogeneous VFI-based NoC-MPSoCs (VFI-NoC-HMPSoC) with DVFS-enabled processors. Unlike the existing population-based optimization algorithms, we proposed a novel population-based algorithm called ARSH-FATI that can dynamically switch between explorative and exploitative search modes at run-time. Our static scheduler ARHS-FATI collectively performs task mapping, scheduling, and voltage scaling. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI-based NoC-MPSoCs. We also developed a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm and gradient descent--inspired voltage scaling algorithm called Energy Gradient Decent (EGD). We introduced a notion of Energy Gradient (EG) that guides EGD in its search for island voltage settings and minimize the total energy consumption. We conducted the experiments on eight real benchmarks adopted from Embedded Systems Synthesis Benchmarks (E3S). Our static scheduling approach ARSH-FATI outperformed state-of-the-art technique and achieved an average energy-efficiency of ∼24% and ∼30% over CA-TMES-Search and CA-TMES-Quick, respectively.

Development of а software framework for real-time management of intelligent devices

The subject of this paper is development of software framework for real-time management of intelligent devices. The framework enables intelligent management of IoT devices in cyber-physical systems using models based on recurrence relations and differential equations. The platform was developed using Python programming language, Django framework and wide corpus of modules and libraries that supports continuous simulation. The software framework incorporates application programming interface as well, for specification of system behaviour, transmission of input parameters and output results, sending control actions via web services to the IoT system.

Trust Quantification for Networked Cyber-Physical Systems

Cyber-physical systems (CPS) are highly integrated hardware–software devices that electro-mechanical components are tightly coupled with advanced computational algorithms for data collection, processing, communication, and control. Internet of Things is the emerging application of CPS. The main research challenge in designing CPS devices and systems is the quantification of complex system behaviors, such as consciousness, adaptation, and evolution. Particularly trust becomes an important element that affects system behavior in the networked society. To capture the unique human societal and systems aspects of trustworthiness quantification for CPS, in this paper, trustworthiness is measured by the perceptions of ability, benevolence, and integrity quantitatively. Ability measures one’s sensing and reasoning capability and influence to others. Benevolence captures the genuineness of intention and the extent of reciprocity in information exchange. Integrity provides the confidence about system dependability and predictability. A generic probabilistic graph model is developed to represent CPS functionality at mesoscale and demonstrate the perception-based quantification of ability and benevolence. Trust-based CPS network design and optimization are also demonstrated with the metrics of ability and benevolence.

Green Massive Traffic Offloading for Cyber-Physical Systems over Heterogeneous Cellular Networks

While the number of things is growing accompanied with an explosive increase in wireless traffic, network providers are facing a set of challenges, especially that the massive number of devices are expected to communicate over the current cellular networks. In an attempt to relieve network congestion and increase throughput, we turn toward cell shrinking and offloading, a key technology in future 5G networks. Using this potential solution, we are mainly targeting two important issues: i) enabling cyber-physical systems (CPS) communications over cellular networks to provide CPS with several benefits such as ubiquitous coverage, global connectivity, reliability and security; and ii) offloading a proportion of CPS traffic to small cells, which in tun increases the throughput of macrocells, and frees more network resources to other users. Using stochastic geometry, we present an analysis on CPS offloading rate and achievable throughput when small cells base stations (SCBSs) are powered by solar energy. The solar energy harvesting allows SCBSs to offset the costs of serving CPS devices. Our results show the potential benefits for both macrocells and small cells in terms of minimum achievable throughput when the CPS offloading rate is high.

Evaluating Secrecy Outage of Physical Layer Security in Large-Scale MIMO Wireless Communications for Cyber-Physical Systems

Large-scale multiple input multiple output (MIMO) wireless system is regarded as a solution to provide high speed connection for exponentially increasing wireless subscriptions for emerging cyber-physical systems (CPSs) and Internet of Things. In order to realize its full potential, there are several challenges to be addressed to achieve high secrecy rate or data rate. In this paper, we analyze outage probability for secrecy rate in MIMO wireless systems in the presence of eavesdroppers and jammers for CPS devices. Our proposed approach takes into account the impact of jammers while finding the best response to minimize the jamming/interfering effect (or to enhance the secrecy rate) and the impact of eavesdropper in secrecy rates of the users. We present formal analysis for secrecy outage probability and interception probability considering Rayleigh fading scenario. The performance is evaluated by using numerical results obtained from Monte Carlo simulations. Numerical results indicate that the system performance is improved significantly when the users adapt their transmit vectors based on their observed interference values. Furthermore, the secrecy outage probability increases with power of jammer and the secrecy capacity decreases when jammer power increases. We observed that the proposed approach outperforms the other existing approaches.

Extending the internet of energy by a social networking of human users and autonomous agents

The spread of IoT technologies fostered the integration of smart devices in cyber physical systems, where the interactions among devices and with human users become more and more complex and intense. Software agents are a programming paradigm that allows for providing intelligence to devices leveraging context awarness, reasoning techniques and communication mechanisms. In this paper we present the design and development of a social network of intelligent agents, which interact with the users and among them using common services and P2P overlay. The emergent behavior of users and agents, that is the optimal charging schedule of e-cars, is achieved recommending the cheapest charging station that does not affect the global distance to be covered for recharging. It optimizes the waiting time reducing the length of the queue at the charging station and global distance covered by all e-cars.