Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network

The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan

Multiprocessor System-on-Chips based Wireless Sensor Network Energy Optimization

Wireless Sensor Network (WSN) is an integrated part of the Internet-of-Things (IoT) used to monitor the physical or environmental conditions without human intervention. In WSN one of the major challenges is energy consumption reduction both at the sensor nodes and network levels. High energy consumption not only causes an increased carbon footprint but also limits the lifetime (LT) of the network. Network-on-Chip (NoC) based Multiprocessor System-on-Chips (MPSoCs) are becoming the de-facto computing platform for computationally extensive real-time applications in IoT due to their high performance and exceptional quality-of-service. In this thesis a task scheduling problem is investigated using MPSoCs architecture for tasks with precedence and deadline constraints in order to minimize the processing energy consumption while guaranteeing the timing constraints. Moreover, energy-aware nodes clustering is also performed to reduce the transmission energy consumption of the sensor nodes. Three distinct problems for energy optimization are investigated given as follows: First, a contention-aware energy-efficient static scheduling using NoC based heterogeneous MPSoC is performed for real-time tasks with an individual deadline and precedence constraints. An offline meta-heuristic based contention-aware energy-efficient task scheduling is developed that performs task ordering, mapping, and voltage assignment in an integrated manner. Compared to state-of-the-art scheduling our proposed algorithm significantly improves the energy-efficiency. Second, an energy-aware scheduling is investigated for a set of tasks with precedence constraints deploying Voltage Frequency Island (VFI) based heterogeneous NoC-MPSoCs. A novel population based algorithm called ARSH-FATI is developed that can dynamically switch between explorative and exploitative search modes at run-time. ARSH-FATI performance is superior to the existing task schedulers developed for homogeneous VFI-NoC-MPSoCs. Third, the transmission energy consumption of the sensor nodes in WSN is reduced by developing ARSH-FATI based Cluster Head Selection (ARSH-FATI-CHS) algorithm integrated with a heuristic called Novel Ranked Based Clustering (NRC). In cluster formation parameters such as residual energy, distance parameters, and workload on CHs are considered to improve LT of the network. The results prove that ARSH-FATI-CHS outperforms other state-of-the-art clustering algorithms in terms of LT.University of Derby, Derby, U

FPGA Implementation of Data Flow Graphs for Digital Signal Processing Applications

A rapid growth in digital signal processing applications has increased the requirement for high-speed digital systems. Multiprocessor systems are the best choice for these applications. A prior sequence of operations should be applied to the operations that described the nature of these applications before hardware implementation is produced. These operations should be scheduled and hardware allocated. This paper proposes a new scheduling technique for digital signal processing (DSP) applications has been represented by data flow graphs (DFGs). In addition, hardware allocation is implemented in the form of embedded system. A proposed scheduling technique also achieves the optimal scheduling of a DFG at design time. The optimality criteria considered in this algorithm are the maximum throughput within the available hardware resources. The maximum throughput is achieved by arranging the DFG nodes according to their inter-related data dependencies. Then, two nodes can be clustered into one compound task to reduce the overall execution time by minimizing the number of tasks to be executed that minimizing the number of cycles to execute them. Then each task is presented in form of instruction to be executed in the hardware system. A hardware system is composed of one or multiple homogenous pipelined processing elements and it is designed to meet the maximum-rate schedule.  Two implementations are proposed of the system architecture according to the number of the processing elements, namely:  the serial system and the parallel system. The serial system comprises one processing element where all tasks are processed sequentially, whilst the parallel system has four processing elements to execute tasks concurrently. These systems consist mainly of seven units: central shared memory, state table, multiway function unit buffer, execution array, processing element/s, instruction buffer and the address generation unit. The hardware components were built on an FPGA chip using Verilog HDL. In synthesis results, the parallel system has better system performance by 25.5% than the serial system. While the serial system requires smaller area size, which described by the number of slice registers and the number of the slice lookup tables (LUTs) than the parallel one. The relationship between the number of instructions that are executed in both systems, and the system area and the system performance that presented by system frequency, are studied. By increasing memories size in both systems, the system performance isn’t affected as in a serial system, and it is slightly decreased as the parallel system by 1.5% to 4.5%. In terms of the systems area, both serial system area and parallel system area are increased and in some cases are doubled. The proposed scheduling technique is shown to outperform the retaining technique, which we have chosen to compare with.  The serial system has better performance by 19.3% higher system frequency than a retiming technique. And the parallel system also outperforms the retaining technique by 51.2% higher system frequency in synthesis results

QoS-aware predictive workflow scheduling

This research places the basis of QoS-aware predictive workflow scheduling. This research novel contributions will open up prospects for future research in handling complex big workflow applications with high uncertainty and dynamism. The results from the proposed workflow scheduling algorithm shows significant improvement in terms of the performance and reliability of the workflow applications

QoS and security-aware task assignment and scheduling in real-time systems

Security issues in mission-critical real-time systems (e.g., command and control systems) are becoming increasingly important as there are growing needs for satisfying information assurance in these systems. In such systems, it is important to guarantee real-time deadlines along with the security requirements (e.g., confidentiality, integrity, and availability) of the applications. Traditionally, resource management in real-time systems has focused on meeting deadlines along with satisfying fault-tolerance and/or resource constraints. Such an approach is inadequate to accommodate security requirements into resource management algorithms. Based on the imprecise computation paradigm, a task can have several Quality of Service (QoS) levels, higher QoS result incurs higher computational cost. Similarly, achieving a higher level of confidentially requires stronger encryption, which incurs higher computational cost. Therefore, there exists a tradeoff between schedulability of the tasks on the one hand, and the accuracy (QoS) and security of the results produced on the other hand. This tradeoff must be carefully accounted in the resource management algorithms. In this context, this dissertation makes the following contributions: (i) formulation of scheduling problems accounting both deadline and security requirements of workloads in real-time systems, (ii) development of novel task allocation and scheduling algorithms for such workloads, (iii) and evaluation of the results through simulation studies and a limited test evaluations in one case. In particular, the following are the three key contributions. Firstly, the problem of scheduling a set of non-preemptable real-time tasks with security and QoS requirements with the goal of maximizing integrated QoS and security of the system is addressed. This problem is formulated as MILP, and then its complexity is proved to be NP-hard. An online efficient heuristic algorithm is developed as the problem is NP-hard. Simulation studies for a wide range of workload scenarios showed that the proposed algorithm outperforms a set of baseline algorithms. Further, the proposed algorithm\u27s performance is close to the optimal solution in a specific special case of the problem. Secondly, a static assignment and scheduling of a set of dependent real-time tasks, modeled as Directed Acyclic Graph (DAG), with security and QoS requirements in heterogeneous real-time system with the objective of maximizing Total Quality Value (TQV) of the system is studied. This problem is formulated as MINLP. Since this problem is NP-hard, a heuristic algorithm to maximize TQV while satisfying the security constraint of the system is developed. The proposed algorithm was evaluated through extensive simulation studies and compared to a set of baseline algorithms for variations of synthetic workloads. The proposed algorithm outperforms the baseline algorithms in all the simulated conditions for fully-connected and shared bus network topologies. Finally, the problem of dynamic assignment and scheduling of a set of dependent tasks with QoS and security requirements in heterogeneous distributed system to maximize the system TQV is addressed. Two heuristic algorithms to maximize TQV of the system are proposed because the problem is NP-hard. The proposed algorithms were evaluated by extensive simulation studies and by a test experiment in InfoSpher platform. The proposed algorithms outperform the baseline algorithms in most of the simulated conditions for fully-connected and shared bus network topologies

A general real-time control approach of intrusion response for industrial automation systems

Intrusion response is a critical part of security protection. Compared with IT systems, industrial automation systems (IASs) have greater timeliness and availability demands. Real-time security policy enforcement of intrusion response is a challenge facing intrusion response for IASs. Inappropriate enforcement of the security policy can influence normal operation of the control system, and the loss caused by this security policy may even exceed that caused by cyberattacks. However, existing research about intrusion response focuses on security policy decisions and ignores security policy execution. This paper proposes a general, real-time control approach based on table-driven scheduling of intrusion response in IASs to address the problem of security policy execution. Security policy consists of a security service group, with each type of security service supported by a realization task set. Realization tasks from several task sets can be combined to form a response task set. In the proposed approach, first, a response task set is generated by a nondominated sorting genetic algorithm (GA) II with joint consideration of security performance and cost. Then, the system is reconfigured through an integrated scheduling scheme where system tasks and response tasks are mapped and scheduled together based on a GA. Furthermore, results from both numerical simulations and a real-application simulation show that the proposed method can implement the security policy in time with little effect on the system