Overlay Virtualized Wireless Sensor Networks for Application in Industrial Internet of Things: A Review
Abstract
:1. Introduction
- A comprehensive comparison of the different techniques used for implementing VWSN is provided under different categories including the various operating systems used, the middleware, and also the virtual machines used.
- The concept of OVWSN is thoroughly discussed as a potential solution to some of the problems encountered in smart industrial applications.
- Peer-to-peer topologies are well contrasted with ring topologies mainly as possible enablers for OVWSN.
- Some research challenges and potential solutions in OVWSN are also discussed for easy comprehension by budding researchers in the OVWSN study area.
- A conceptualization of the design requirements for future OVWSN has also been provided and discussed.
2. Virtualization in Wireless Sensor Networks
2.1. Operating Systems
- (1)
- SenSmart: SenSmart is a sensor based OS that supports simultaneous application tasks in resource constrained nodes [38]. In order to provide concurrent execution of different application tasks, SenSmart is designed with a stack allocation system that is managed dynamically at run time. This enables an unused stack space to be reclaimed from expired tasks that no longer require it. When a new task is initiated to run, the content of the current task is compressed and saved in a circular buffer for its resumption. This mechanism typically supports the concept of virtualization in WSN as it enables more nodes to access limited system resources as required. SenSmart is an event-driven programming model and thus follows a sense-and-send workflow model. This further supports its use in VWSN. It has been implemented in some hardware platforms including Mica2/MicaZ [38]. However, it is found that SenSmart uses more CPU cycles for same applications than the TinyOS.
- (2)
- RIOT: RIOT is an Internet of Things (IoT) specialized OS designed to support the use of diverse hardware resources in the IoT [30]. Its main aim is to provide real-time multithreading support, ensure a friendly programming model, while providing support for resource-constrained devices using low power consumption transmission technologies. RIOT is still a work in progress with no technical performance comparisons with existing Oss [30]. However, regarding VWSN, RIOT uses a realtime thread-based programming model in which different services are encoded in standard ANSI C/C++ languages to run in parallel. Thus, application tasks are encoded independently of the hardware and software in order to run them on different devices. This is a key feature required for VWSN.
- (3)
- SenSpire: SenSpire is an event-driven and thread-based programming model [39]. SenSpire adopts a multilayer abstraction approach in order to develop networked applications. Regarding VWSN, SenSpire ensures that tasks can be programmed as events or as threads. In this case, event tasks typically have higher priority than thread tasks [39]. This ensures that the OS reacts more to external requests for system resources, thus facilitating broader use of the same system resource. It has less interrupt latency than the TinyOS, although with more overhead scheduling delay than the MANTIS OS.
- (4)
- PAVENET: This is a thread-based OS for handling issues regarding preemption of multithreaded application tasks [35]. Its use is highly limited to the PIC18 microchip and cannot be deployed on other hardware platforms such as MICAZ. In order to support VWSN, the PAVENET OS supports thread-based programming and the use of C language. It is possible to ensure varying priority levels via the use of programmed multithreaded applications [35]. The main limitation of PAVENET is its lack of portability across diverse hardware platform.
- (5)
- MANTIS: MANTIS is also a thread-based embedded OS that supports concurrent execution on sensor nodes [36]. It is considered for VWSN because it is completely thread-based and typically easy to program without the need to manage low-level details of the stack/memory. The time-sliced multithreading approach ensures that several application tasks can run concurrently without using a run-to-completion model [36].
- (6)
- LiteOS: It is a Unix-like OS particularly considered for sensor nodes [33]. It adopts a hierarchical file system with a command shell that works wirelessly. LiteOS is highly flexible for VWSN because it uses a hybrid programming model that combines both simultaneous execution of application threads and events through a call-back mechanism. Application tasks can be programmed in C language [33]. Installation and the execution of application tasks is very simple and can be accomplished by dynamically copying user applications. It is highly viable for deployment in VWSN.
- (7)
- Contiki: It is one of the most popular OSs for WSN. It provides the concept of protothreads, which combines the concepts of event-driven and thread-based approaches [29]. This allows applications and services to be dynamically uploaded/unloaded wirelessly on sensor nodes. For VWSN, Contiki is highly applicable because it supports multiple applications that are typically independent of the OS and can invariably run on top of it. Applications can be programmed in C language and updated/installed without reinstalling the entire OS.
- (8)
- TinyOS: It is an application-specific, component-based OS that is event-driven and offers a flexible platform for innovation [31]. It is written in a variant of C-language called nesC. It may not necessarily be the most viable for VWSN because it is mainly event-driven. However, efforts are currently underway to create variants that may be suitable for VWSN.
2.2. Middleware and Virtual Machine-Based Approaches
- (1)
- VMSTAR: It is a Java-based software framework for developing application-specific virtual machines [81]. It supports the sequential and simultaneous use of thread-based applications. For VWSN, VMSTAR does not support the simultaneous use of multi-thread application tasks, instead, it supports only single-threaded Java applications. However, concurrent events can be handled using action listeners [81]. This can be used to identify high priority threads so that expired threads can be relived of system resources to cater for other application tasks.
- (2)
- Squawk: This is also a Java virtual machine that runs on sensor hardware [84]. Different from VMSTAR, Squawk does not require an OS in order to run, instead, all its basic requirements are inbuilt. For VWSN, Squawk adopts a different approach compared to other solutions. First, it provides an application isolation mechanism, which enables multiple application tasks to be treated as Java objects [84]. Thus, applications can have multiple threads, which are managed by the Java Virtual Machine (JVM).
- (3)
- Agilla: It is a mobile agent-based middleware that runs over the TinyOS along with a VM engine to conduct sequential execution of multiple applications [80]. This is normally done in a round robin manner. For VWSN, Agilla depends on the TinyOS in order to provide simultaneous execution of tasks. It also guarantees this via the mobile agents executed in a round-robin style [80]. However, the difficulty in the programming language adopted by Agilla typically limits its use for VWSN. It adopts a low-level assembly-like language, which can be very difficult to modify or to build upon.
- (4)
- UMADE: UMADE is a mechanism provided to promote fair utilization of resources among multiple contending applications [83]. It is typically built based on the Agilla VM and the TinyOS. For VWSN, UMADE typically uses Agilla for virtualization, while extending Agilla in order to provide dynamic memory management for concurrent applications.
- (5)
- Nano-CF: It is a macro-programming framework for in-network programming and execution of multiple applications in WSN [44]. It adopts a proprietary OS called Nano-RK operating system, which enables several applications to use a common WSN architecture. This makes it suitable for VWSN. For VWSN, it allows independent application developers to write application tasks for a common WSN infrastructure [44]. These application tasks run independently and are not coupled to the sensor OS. It highly suited for data acquisition with sensor nodes having multiple on-board sensors.
2.3. Node Virtualization
- (1)
- Event-driven programming model is more prevalently adopted for VWSN than the threaded-driven model. The is prevalence may be because VWSN nodes need to stay in the idle or sleep mode and may only be required to transmit data whenever there is a significant change in the parameter(s) being monitored. This typically makes the event-driven model more power preserving than the threaded mode. Thus, nodes can easily send signals at longer time intervals (for example, in 24 h intervals) in order to inform the network about their continuous existence.
- (2)
- The event driven model is typically slower in execution than the threaded driven model. This makes it quite poor in managing VWSNs in highly dynamic resource environments [9]. On the other hand, a few notable threaded programming models are more capable of resource discovery, for example, the RIOT platform. For this reason, frameworks such as the RIOT platform are typically encouraged for the implementation of VWSNs.
- (3)
- It is noted in Table 3 that some platforms lack resource discovery and publication services, which are very critical requirements in the management of the entry of new nodes into the network. Thus, platforms or frameworks, which are typically desired for VWSNs should possess dynamic management capabilities for efficient resource distribution in virtual environments.
- (4)
- Most platforms for VWSN are typically OS based. This implies that there is a greater trend towards the use of OS-based solutions than the use of virtual-machine based solutions. This may be attributed to the higher cost of development associated with using VM than OS based solutions.
- (5)
- Based on the examination metrics adopted in Table 3, it is quickly noted that Contiki possesses more desirable characteristics than the other platforms. It notably supports more protocols based on its unique programming model, which enables it to easily combine both the event and threaded driven models. A close competitor to the Contiki platform is the RIOT platform notable for its ability to perform resource discovery. Thus, the Contiki platform may be a more generalized model to adopt in VWSN designs.
- (6)
2.4. Network Virtualization
3. Virtualization: A Solution to Some Challenges in WSN
3.1. Security
3.2. Scalability
3.3. Quality of Service (QoS)
3.4. Fault-Tolerance
3.5. Robustness
3.6. Heterogeneity
4. Virtualization in WSN and the Concept of Overlay Networks
4.1. Topology
4.2. Peer-to-Peer Topology
4.3. Ring Topology
4.4. Routing
4.5. Media Access
4.6. Service Discovery
4.7. Resource Allocation and Utilization
4.8. Summary
5. Overlay Virtualized WSN: Some Design Requirements
- (1)
- Multiple application execution—The loose coupling between the application tasks in the overlay network and the underlying infrastructure enables dynamic resource sharing and for several applications to utilize same hardware with the illusion of lone ownership, which forms the core of shared sensor systems.
- (2)
- Robust and IoT ready routing protocols—Several lightweight routing protocols exist in the IoT domain that are poised to take the lead in framing the standard protocols for data exchange in WSN. A few protocols like CoAP [153] have exhibited dynamic response to their operation in virtual frameworks as well as in overlay networks.
- (3)
- Resource-rich nodes—Recent advances in silicon technologies have produced a rise in the number resource rich sensor nodes being used for WSN. With the increase in the demand for shared sensor systems, it is imperative that resource rich nodes allow for energy efficiency, yet producing high throughput. Sensing applications such as in video, seismic, terrestrial and volcanic activities suggest that high- end devices are needed to process these application areas. Consequently, multi-core embedded systems [151] are noted to have contributed to the progress in resource-rich physical layers. The aggregation of data before it is sent to the sink node is also required for these nodes to avoid redundant data being sent to the node. Only significant data/information is sent, which could signify a change in status from the previous value.
6. Overlay Virtualized WSN: Open Research Challenges
6.1. Real-Time Performance
6.2. Advanced Node Virtualization
6.3. Publication and Discovery
6.4. Simulation Tools
6.5. Task and Sensor Node Assignment
6.6. Evolution of the Framework
6.7. Abstraction Support
6.8. Energy Efficiency
6.9. Security, Resource Management and Allocation
6.10. Process Scheduling
6.11. Challenges from Other Emerging Technologies
7. Conclusions
Author Contributions
Acknowledgments
Conflicts of Interest
References
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Platform | Contiki | TinyOS | MANTIS | OpenWSN | LiteOS |
---|---|---|---|---|---|
Real-Time | No | No | No | No | No |
Hardware Platforms | ESB, TelosB, Tmote Sky | MICA (z)(2), TelosB, Iris, Shimmer | MICA(2)(z), Telos, MANTIS nymph | TelosB, GINA, WSN430, Z1, OpenMoteCC2538 | MICAz, IRIS |
Virtualization | Serial Execution | Yes | Semaphores | Yes | Synchronization primitives |
Static or Dynamic | Dynamic | Static | Dynamic | Dynamic | Dynamic |
Network Support | uIP, uIP6, Rime | Active message | Comm | 6LoWPAN, RPL, CoAP | Message-based |
Simulation | Cooja. MSP-Sim, NetSim | TOSSIM, Viptos, Qualnet | XMOS | Open Visualizer, OpenSim | AVRORA |
OTA | Yes | Yes | No | Yes | Yes |
Latest build | 2.2.1 | 2.0 | 1.0 Beta | 1.8.02 | 1.0 |
Multi-threads | Yes | Tiny-threads | Yes | Yes | Yes |
Release date | 2004 | 2000 | 2005 | 2011 | 2008 |
Concurrent execution | Yes | Yes | Yes | Yes | No |
References | [52,53,54] | [31,55,56] | [36,57,58] | [32,59] | [37] |
Platform | SenShare | Pavenet | Agilla | Squawk VM | VMStar |
---|---|---|---|---|---|
Programming Model | Event-driven | Thread | Tuple-space and mobile agents | Thread | Thread |
Real-time Performance | Yes | Yes | No | No | No |
Communication Protocols | CTP | Not discussed | Not discussed | 6LoWPAN, CTP, LQRP | Not discussed |
Decoupling | Yes | No | Yes | No | No |
Programming Language | nesC | C | Assembly | J2ME | Java |
Platform | Programming Model | Resource Discovery | Type | Heterogeneity | Platform Independence | Multi-radio Support | Programming Language | Protocols |
---|---|---|---|---|---|---|---|---|
Contiki [99] | Protothreads | No | OS | Yes | Yes | Yes | C | HTTP, COAP, UDP, TCP, RPL, 6LoWPAN |
RIOT [30] | Threaded | Yes | OS | Yes | Yes | Yes | ANSI C/C++ | 6LoWPAN, RPL |
TinyOS [31] | Event-driven | No | OS | Yes | Yes | Yes | nesC | 6LoWPAN, ZigBee |
OpenWSN [32] | State-machine | No | OS | Yes | No | No | C | 6LoWPAN, RPL, CoAP |
FreeRTOS [37] | Threaded | No | OS | Yes | Yes | No | C | Third-party network stacks |
VMStar [81] | Threaded | No | VM | No | No | No | Java | NA |
SenaaS [82] | Event-driven | No | VM | Yes | Yes | No | NA | NA |
SenSmart [38] | Event-driven | No | OS | Yes | Yes | No | nesC | NA |
SenSpire [39] | Event-driven and threaded | No | OS | Yes | Yes | No | CSpire | CSMA, CSMA/CA, B-MAC, X-MAC |
Agilla [80] | Tuple space and mobile agent | Yes | VM | Yes | No | No | Assembly-like | NA |
LiteOS [33] | Event-driven and Threaded | No | OS | Yes | Yes | No | C | NA |
PAVENET [86] | Threaded | No | OS | No | No | No | C | NA |
MANTIS [36] | Threaded | No | OS | No | No | No | C | TDMA |
UMADE [83] | Event-driven | No | VM | No | No | No | nesC | NA |
Squawk VM [83] | Threaded | No | VM | No | Yes | No | J2ME | CTP, 6LoWPAN, AODV, LQRP |
Nano-CF [44] | Event-driven | No | VM | Yes | Yes | No | Nano-CL | DSR, TDMA, B-MAC |
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Nkomo, M.; Hancke, G.P.; Abu-Mahfouz, A.M.; Sinha, S.; Onumanyi, A.J. Overlay Virtualized Wireless Sensor Networks for Application in Industrial Internet of Things: A Review. Sensors 2018, 18, 3215. https://doi.org/10.3390/s18103215
Nkomo M, Hancke GP, Abu-Mahfouz AM, Sinha S, Onumanyi AJ. Overlay Virtualized Wireless Sensor Networks for Application in Industrial Internet of Things: A Review. Sensors. 2018; 18(10):3215. https://doi.org/10.3390/s18103215
Chicago/Turabian StyleNkomo, Malvin, Gerhard P. Hancke, Adnan M. Abu-Mahfouz, Saurabh Sinha, and Adeiza. J. Onumanyi. 2018. "Overlay Virtualized Wireless Sensor Networks for Application in Industrial Internet of Things: A Review" Sensors 18, no. 10: 3215. https://doi.org/10.3390/s18103215
APA StyleNkomo, M., Hancke, G. P., Abu-Mahfouz, A. M., Sinha, S., & Onumanyi, A. J. (2018). Overlay Virtualized Wireless Sensor Networks for Application in Industrial Internet of Things: A Review. Sensors, 18(10), 3215. https://doi.org/10.3390/s18103215