Resource-constrained Smart Devices Research Articles

A Lightweight Anonymous Authentication and Key Negotiation Scheme in Smart Home Environments

With the rapid development of Internet of Things (IoT) technology, smart home users can access and control smart devices remotely to enjoy convenient and efficient services. However, sensitive data collected by smart devices is vulnerable to attacks such as eavesdropping and simulation when transmitted through public channels. At the same time, the security of resource-constrained smart devices is low, and attackers may use the controlled devices to carry out malicious operations further. To address the aforementioned existing security issues, this paper proposes a lightweight user anonymous authentication scheme for resource-constrained smart home environments. At the same time, the security analysis is carried out to further prove the proposed scheme's security. Finally, the performance analysis between the proposed scheme and the existing similar schemes proves that the proposed scheme has advantages in calculation cost and safety characteristics.

Toward Secure IoT Networks in Healthcare Applications: A Game-Theoretic Anti-Jamming Framework

The Internet of Things (IoT) is used to interconnect a massive number of heterogeneous resource-constrained smart devices. This makes such networks exposed to various types of malicious attacks. In particular, jamming attacks are among the most common harmful attacks to IoT networks. Therefore, an anti-jamming power allocation (PA) strategy is first proposed in this article for health monitoring IoT networks by exploiting the game theory to minimize the worst case jamming effect under multichannel fading. This strategy uses an iterative algorithm based on gradient descent to identify the Nash Equilibrium (NE) of the game. An artificial neural network (ANN) model is also proposed to accelerate the convergence of the algorithm making it more suitable for IoT networks. Furthermore, novel data population (DP), extension, and balancing techniques are proposed to enhance the efficiency of the proposed strategy in combating jamming attacks even for network configurations that were never used in the training phase. In addition, time and spatial diversities are exploited using a heterogeneous iterative algorithm to enhance the security of the network.

Energy consumption-based services composition optimization for internet of things

Internet of Things (IoT) services are directly deployed on resource-constrained smart devices. Smart devices are characteristic by spatial and temporal constraints and energy limitations. A single IoT service cannot meet the complex requirements of users, so multiple IoT services need to be combined to provide services to users. As more and more smart devices are deployed in IoT, how to select IoT services with lower energy consumption and better quality of service (QoS) for service composition becomes a challenging problem. Combined with the characteristics that the data information of IoT is closely related to geographical location, the GeoHash algorithm is used to locally screen services based on geographical location, so as to narrow the range of candidate services. For smart devices with energy constraints, this paper proposes a combined optimization model. The model considers not only the transmission energy consumption and switching energy consumption, but also the execution energy consumption when the device provides services. In order to balance QoS attributes and energy consumption, the composition problem is regarded as a multi-objective optimization problem and solved using a genetic algorithm (GA). The simulation results show that service composition scheme selected by this service composition optimization model has lower energy consumption and higher service quality.

PUF-Assisted Lightweight Group Authentication and Key Agreement Protocol in Smart Home

Various IoT-based applications such as smart home, intelligent medical, and VANETs have been put into practical utilization. The smart home is one of the most concerned environments, allowing users to access and control smart devices via the public network remotely. The smart home can provide many intelligent services for users through these smart devices. To securely access devices and obtain collected data over the public network, multifactor authentication protocols for smart home have gained wide attention. However, most of these protocols cannot withstand impersonation attack, smart device lost attack, privileged-insider attack, smart card lost attack, and so on. Besides, high communication and computational costs weaken the system performance, which leads to most authentication protocols are not suitable for resource-constrained smart devices. To mitigate the aforementioned drawbacks, we proposed a PUF-assisted lightweight group authentication and key agreement protocol to implement secure access to multiple devices in the smart home simultaneously using the Chinese Remainder Theorem and secret sharing technique. Our protocol also utilizes physical unclonable function (PUF) and fuzzy extractor technique to extract the digital fingerprint of the smart devices, which can uniquely validate smart devices and protect the secrets stored in their memory. Our protocol can support various security features and withstand the many well-known attacks in the smart home. The performance analysis indicates that the proposed protocol can efficiently reduce communication/computational costs when the user simultaneously accesses multiple devices.

Computation Offloading for Distributed Mobile Edge Computing Network: A Multiobjective Approach

Mobile edge computing (MEC) is emerging as a cornerstone technology to address the conflict between resource-constrained smart devices (SDs) and the ever-increasing computational demands of the mobile applications. MEC enables the SDs to offload computational-intensive tasks to the nearby edge nodes for providing better quality-of-services (QoS). The recently proposed offloading strategies, mainly consider a centralized approach for a limited number of SDs. However, with the growing popularity of the SDs, these offloading models may have the scalability issue and can be susceptible to single point failure. Although there are few distributed offloading models in the literature, they ignore the vast computational resources of the cloud, load sharing between the MEC servers, and other optimization parameters. Toward this end, we propose an efficient computation offloading scheme for a distributed load sharing MEC network in cooperation with cloud computing to enhance the capabilities of the SDs. We formulate a nonlinear multiobjective optimization problem by applying queuing theory to model the execution delay, energy consumption, and payment cost for using edge and cloud services. To solve the formulated problem, we propose a stochastic gradient descent (SGD) algorithm based solution approach to jointly optimize the offloading probability and transmission power of the SDs for finding an optimal trade-off between energy consumption, execution delay, and cost of the SDs. Finally, we perform extensive simulations to demonstrate the effectiveness of the proposed offloading scheme. Moreover, compared to the other solutions, the proposed scheme is scalable and outperforms the existing schemes.

A Secured Framework for SDN-Based Edge Computing in IoT-Enabled Healthcare System

The Internet of Things (IoT) consists of resource-constrained smart devices capable to sense and process data. It connects a huge number of smart sensing devices, i.e., things, and heterogeneous networks. The IoT is incorporated into different applications, such as smart health, smart home, smart grid, etc. The concept of smart healthcare has emerged in different countries, where pilot projects of healthcare facilities are analyzed. In IoT-enabled healthcare systems, the security of IoT devices and associated data is very important, whereas Edge computing is a promising architecture that solves their computational and processing problems. Edge computing is economical and has the potential to provide low latency data services by improving the communication and computation speed of IoT devices in a healthcare system. In Edge-based IoT-enabled healthcare systems, load balancing, network optimization, and efficient resource utilization are accurately performed using artificial intelligence (AI), i.e., intelligent software-defined network (SDN) controller. SDN-based Edge computing is helpful in the efficient utilization of limited resources of IoT devices. However, these low powered devices and associated data (private sensitive data of patients) are prone to various security threats. Therefore, in this paper, we design a secure framework for SDN-based Edge computing in IoT-enabled healthcare system. In the proposed framework, the IoT devices are authenticated by the Edge servers using a lightweight authentication scheme. After authentication, these devices collect data from the patients and send them to the Edge servers for storage, processing, and analyses. The Edge servers are connected with an SDN controller, which performs load balancing, network optimization, and efficient resource utilization in the healthcare system. The proposed framework is evaluated using computer-based simulations. The results demonstrate that the proposed framework provides better solutions for IoT-enabled healthcare systems.

SmartEdge: An end-to-end encryption framework for an edge-enabled smart city application

The Internet of Things (IoT) has the potential to transform communities around the globe into smart cities. The massive deployment of sensor-embedded devices in the smart cities generates voluminous amounts of data that need to be stored and processed in an efficient manner. Long-haul data transmission to the remote cloud data centers leads to higher delay and bandwidth consumption. In smart cities, the delay-sensitive applications have stringent requirements in term of response time. To reduce latency and bandwidth consumption, edge computing plays a pivotal role. The resource-constrained smart devices at the network core need to offload computationally complex tasks to the edge devices located in their vicinity and have relatively higher resources. In this paper, we propose an end-to-end encryption framework, SmartEdge, for a smart city application by executing computationally complex tasks at the network edge and cloud data centers. Using a lightweight symmetric encryption technique, we establish a secure connection among the smart core devices for multimedia streaming towards the registered and verified edge devices. Upon receiving the data, the edge devices encrypts the multimedia streams, encodes them, and broadcast to the cloud data centers. Prior to the broadcasting, each edge device establishes a secured connection with a data center that relies on the combination of symmetric and asymmetric encryption techniques. In SmartEdge, the execution of a lightweight encryption technique at the resource-constrained smart devices, and relatively complex encryption techniques at the network edge and cloud data centers reduce the resource utilization of the entire network. The proposed framework reduces the response time, security overhead, computational and communication costs, and has a lower end-to-end encryption delay for participating entities. Moreover, the proposed scheme is highly resilient against various adversarial attacks.

Time and Attribute Based Dual Access Control and Data Integrity Verifiable Scheme in Cloud Computing Applications

In the era of big data, cloud-based industrial applications can provide available and convenient data access for resource-constrained smart devices. Attribute-based encryption can be used to ensure data security while providing fine-grained data access. However, current attribute-based encryption schemes rarely consider the access control of time, and the integrity verification of data simultaneously. In response to the above two problems, we propose a time and attribute based dual access control and data integrity verifiable scheme in cloud computing applications (DCDV). Firstly, a hierarchical time tree is introduced in the attribute-based encryption technology by using of hierarchical identity-based encryption technology to set an effective access time period and a specified decryptable time period for the user’s attributes key and encrypted data separately. The decryption operation can only be performed if the attribute set of user satisfies the data owner’s access policy and the effective access time period of the user’s attributes key completely covered the decryption time period set by the data owner. In this way, the data is dual controlled with time and attributes to solve the problem of privacy data leakage caused by private key leakage. Secondly, by using of the inverted index and Merkle hash tree, the data verification tree is designed. The data user can verify the integrity of the ciphertext data returned by the cloud server without decryption, which solves the problem that the cloud server may delete or modify the data. Finally, the security proof and efficiency analysis show that our scheme is secure and practical.

Task offloading and resource allocation for edge-of-things computing on smart healthcare systems

Impelled by prevalent smart devices and omnipresent wireless communication networks, Edge-of-things transpires as a captivating paradigm to accommodate power-sensitive or compute-intensive applications over resource-constrained smart devices. In this research, we focus on flexible compute-intensive task offloading to a local cloud (i.e., cloudlet) saving energy, which aims to optimize the energy consumption, the operation speed, and the cost. A fruit fly optimization based task offloading algorithm (FOTO) is proposed, which improves offloading and resources allocation to acquire the nominal energy consumption under the existing restraints. Performances are evaluated regarding energy consumption, execution time and cost, which are compared with the cooperative multi-tasks scheduling based on ant colony optimization algorithm (CMS-ACO) and heuristic queue based algorithm (GA-ACO). The experimental results prove the effectiveness of proposed FOTO algorithm by comparing with existing algorithms.

Mobile Edge Computing and Networking for Green and Low-Latency Internet of Things

IoT, a heterogeneous interconnection of smart devices, is a great platform to develop novel mobile applications. Resource constrained smart devices, however, often become the bottlenecks to fully realize such developments, especially when it comes to intensive-computation-oriented and low-latency-demanding applications. MEC is a promising approach to address such challenges. In this article, we focus on MEC applications for IoT, and address energy efficiency as well as offloading performance of such applications in terms of end-user experience. In this regard, we present a mobility-aware hierarchical MEC framework for green and low-latency IoT. We deploy a game theoretic approach for computation offloading in order to optimize the utility of the service providers while also reducing the energy cost and the task execution time of the smart devices. Numerical results indicate that the proposed scheme does brings significant enhancement in both energy efficiency and latency performance of MEC applications for IoT.

Cloud is safe when compressive: Efficient image privacy protection via shuffling enabled compressive sensing

Cloud-assisted image services are widely used for various applications. Due to the high computational complexity of existing image encryption techniques, privacy protection becomes extremely challenging for resource-constrained smart devices. We propose eCIS, a cloud-assisted image service where compression and encryption are jointly used for image processing efficiency. eCIS could shift the high computational cost from encoder and decoder to the cloud while providing efficient and adaptive image privacy protection for end users. The key idea is to leverage different measurement matrices for sampling device and cloud. Thus encryption is realized with shuffling enabled compressive sensing. We conduct in-depth theoretical analysis and demonstrate the effectiveness with evaluations. To this end, extensive experimental results show that eCIS can effectively protect image privacy and meet user’s adaptive security requirements. Meanwhile, our experimental results show that eCIS could significantly save the system running time compared with existing cloud-assisted schemes.

Design and Implementation of Warbler Family of Lightweight Pseudorandom Number Generators for Smart Devices

With the advent of ubiquitous computing and the Internet of Things (IoT), the security and privacy issues for various smart devices such as radio-frequency identification (RFID) tags and wireless sensor nodes are receiving increased attention from academia and industry. A number of lightweight cryptographic primitives have been proposed to provide security services for resource-constrained smart devices. As one of the core primitives, a cryptographically secure pseudorandom number generator (PRNG) plays an important role for lightweight embedded applications. The most existing PRNGs proposed for smart devices employ true random number generators as a component, which generally incur significant power consumption and gate count in hardware. In this article, we present Warbler family, a new pseudorandom number generator family based on nonlinear feedback shift registers (NLFSRs) with desirable randomness properties. The design of the Warbler family is based on the combination of modified de Bruijn blocks together with a nonlinear feedback Welch-Gong (WG) sequence generator, which enables us to precisely characterize the randomness properties and to flexibly adjust the security level of the resulting PRNG. Some criteria for selecting parameters of the Warbler family are proposed to offer the maximum level of security. Two instances of the Warbler family are also described, which feature two different security levels and are dedicated to EPC C1 Gen2 RFID tags and wireless sensor nodes, respectively. The security analysis shows that the proposed instances not only can pass the cryptographic statistical tests recommended by the EPC C1 Gen2 standard and NIST but also are resistant to the cryptanalytic attacks such as algebraic attacks, cube attacks, time-memory-data tradeoff attacks, Mihaljević et al.’s attacks, and weak internal state and fault injection attacks. Our ASIC implementations using a 65nm CMOS process demonstrate that the proposed two lightweight instances of the Warbler family can achieve good performance in terms of speed and area and provide ideal solutions for securing low-cost smart devices.

WG-8: A Lightweight Stream Cipher for Resource-Constrained Smart Devices

Lightweight cryptographic primitives are essential for securing pervasive embedded devices like RFID tags, smart cards, and wireless sensor nodes. In this paper, we present a lightweight stream cipher WG-8, which is tailored from the well-known Welch-Gong (WG) stream cipher family, for resource-constrained devices. WG-8 inherits the good randomness and cryptographic properties of the WG stream cipher family and is resistant to the most common attacks against stream ciphers. The software implementations of the WG-8 stream cipher on two popular low-power microcontrollers as well as the extensive comparison with other lightweight cryptography implementations highlight that in the context of securing lightweight embedded applications WG-8 has favorable performance and low energy consumption.