A CONTEXT-BASED SOFTWARE RECONFIGURABLE SYSTEM FOR WIRELESS SENSOR NETWORK

ABSTRACT

Wireless Sensor Network application entails deploying thousands of wireless sensor nodes in unreachable locations. The inability to reconfigure each node in order to take on new tasks poses a serious challenge to the continued operation of the entire system. Several attempts have been made to address these challenges, of interest is one that exploits design-time knowledge of the application scenario dynamics to construct and implements a proactive runtime reconfiguration paradigm. However, It suffers two defects:   the possibility of capturing all anticipated reconfiguration needs can be challenging, and the scarcely available memory space might not be sufficient to accommodate codes written to address these needs. Moreover, even if it does, there is the likelihood of redundant codes written to handle anticipated changes, which might never occur, and invariably taking up scarcely available memory spaces. This research work explores the use of context information to improve upon wireless sensor networks reconfiguration processes. The research’s aim is to develop  a software system that dynamically reconfigures wireless sensor network operational functionalities optimally based  on  evolving  application  context.  In  order to  demonstrate the benefits  of the context based reconfiguration model, two contexts related input variables were used. The first variable is obtain using a metric tool (PDE) devised for extracting context information  from  the  delta  of  two  files  (application  related  context).  The  second variable entails the battery energy level state of the sensor node taken as an operational- demand related context. A robust inference engine was developed based on the inferred expert knowledge on memory related energy consumption pattern during the reconfiguration process. The pattern studied and presented explains how delta size and its orientation can influence energy consumption while reprogramming sensor nodes. The resulting output from the fuzzy logic system controls when and which one of the reconfiguration approaches should be implemented in order to prolong the battery life. The model’s performance was evaluated on an OMNet++ simulation platform using pilot data obtained from a testbed composed of Microchips’ PIC32MX320F128H microcontroller and MRF24J40MB transceiver. In a network of six nodes, two were equipped with the developed model capability and the others were not. The overall energy expended as read, erase and write were obtained from each node for the purpose of comparison. Results obtained show that 65% of energy expended during the erasure procedure is saved in nodes that adopt the context based reconfiguration model. Similarly, 45% and 69% reduction in energy consumption were obtained for the read and  write  procedures  respectively.  The  research  work  was  able  to  emphasise  the benefits of identifying, employing and managing the impact of contextual information (Application/operational related) during wireless sensor network reconfiguration procedure.

CHAPTER ONE

1.0   INTRODUCTION

Wireless   sensor   network   (WSN)   is   a   collection   of   small-embedded   devices interconnected with the sole aim of sensing, processing, sharing and remotely relaying data  via  known  communication  protocols.  WSN  applications  are  widespread  and increasingly  growing  by  the  day.  Examples  of  these  applications  entail:  military sensing, physical security, air traffic control, traffic surveillance, video surveillance, industrial and manufacturing automation, distributed robotics, health care monitoring and delivery.Others are environmental monitoring, observatory purposes in weather and earthquake monitoring, building and structural monitoring (Chong and Kumar, 2003). The small embedded devices commonly referred to as wireless sensor nodes share or relay data to the base station by employing various communication models. A number of communication models exist as  follows: direct, multi-hop and clustering. The node consists   of sensors,   processing elements (microcontrollers),   radio communication interface  and  a  power  source  (battery  and  solar).  The  sensors  detect  and  measure physical phenomena such as temperature, light, magnetic field, pressure, acceleration, current and ultrasound.

A typical WSN application entails deploying hundreds or thousands of wireless sensor nodes in unreachable locations. Examples of these applications are as follows: surveillance, environmental monitoring, oil and gas pipeline monitoring (Misra and Eronu, 2012). When there is a change in the operational needs of the system or new functionalities are required in such application, reconfiguration of either the entire network or individual sensor nodes become inevitable. The inability to effect these changes could pose a serious challenge to the continued operation of the entire system (Misra and Eronu, 2012). Other issues that could warrant the need for a reconfigurable WSN are bug fixes (Hinkelmann, Reinhardt and Glesner, 2008) , regular code updates (Kulkarni, Sanyal,   Al-Qaheri and Sanyal, 2009), security challenges (Portilla, Otero, De la Torre, Riesgo, Stecklina, Peter and  Langendorfer, 2010), RF communication link (Ramamurthy, Prabhu and Gadh, 2004) and efficient energy management.

Altering system functionality in both real-time or design time involves making changes to either the hardware component or software component or both components. The altering process could in some cases (Krishna, Bagchi and Khalil, 2009) be referred to as  ‘Reprogramming’ ,  and    in  some  other cases  (Muralidhar and  Rao,  2008) it  is considered as ‘Reconfiguration’. When only the software component is involved, it is termed ‘Reprogramming’. Likewise, the term “Reconfiguration” is used when the Hardware components are involved.   Probably in agreement with this proposition, Compton   and   Hauck   (2002)   described   reconfigurable   systems   as   devices   that incorporate some form  of hardware programmability. However, in this  thesis, both terms are used interchangeably. Both terms refer to ‘an act or process of effecting a change’ to the system’s underlying codes or instructions (high-level or low-level languages and Hardware Description Language (HDL)). The aim is to alter its initial functions. Some other words often used to connote reconfiguration in certain literature are ‘updating’ , ‘adaptation’ or ‘Adapting’ (Brown and Sreenan, 2006; Han, Kumar, Shea and Srivastava, 2005).  Stating these definitions clearly prevent misapprehension due to the use of different words or terms meant to explain the same concept.

Good design criteria demand that for a system to be cost-effective, it should possess attributes that enable it take cognisance of the resources around its immediate and remote environments. It should autonomously or remotely be directed to perform new tasks or implement existing task more efficiently.    The adoption of Context-driven and context-aware paradigm in distributed systems is on the increase, as such WSN should not be an exception (Silva and Vuran, 2010).

Sensor network application can be expensive to implement, especially when large-scale projects are involved. Being able to manage network resources and tailor their use towards several other applications other than what they were initially designed for can be a daunting task.   Application objectives, anticipated constraints, resource managerial strategies  and  other surrounding factors,  when  well  spelt  out in the design  model, simplify the complexity arising from adapting WSN to newer applications. Identifying these factors requires a careful analysis of the entire WSN operational environment. When these factors are considered as a source of relevant contextual information, then reconfiguring WSN becomes much easier. In this perspective, the intent is to understudy context-related approaches as they relate to reconfiguration computing and by extension reconfigurable WSN.

Baldauf, Dustdar, and Rosenberg (2007) described context-aware systems as systems that can alter their mode of operation to suit the current context without explicit user intervention thereby increasing the systems usability and effectiveness. Context awareness is commonly used in systems whose operation or responses are influenced by certain defined surrounding factors. The concept of context-aware systems allows applications to gather context data and adapt their operational behaviour accordingly. These applications can function without explicit intervention and thereby increase their usability and effectiveness within the context of the environment where they operate (Baldauf et al., 2007). Context-driven allows a system to assign resources on current and relevant tasks, rather than just processing predefined applications. Equipping the node with relevant context sensing capabilities enables it to estimate future context requirements. When these requirements are used appropriately, the network can be configured to perform more optimally. Management systems can guess about what kind of tasks will be required in the near future and consider it when allocating resources. Hence,  effecting  the  sensor  nodes’  reconfiguration  processes  based  on  contextual information can be helpful in several ways. For example, deciding on when and how to effect a reconfiguration process can result in reducing the system’s operational cost. This cost invariably entails energy consumed and memory size utilised by the nodes during the reconfiguration process.

1.1 Motivation

In order to appreciate the benefit of such a context-based reconfigurable paradigm, consider a WSN application scenario as depicted in Figure 1.1. The application is intended to be deployed and utilised in an urban setting, and the nodes have the capability to reconfigure themselves autonomously. In addition, they can as well be remotely reconfigured to use any desired particular communication standard (RFID, Bluetooth, UWB, Zigbee, GSM, GPRS, WIFI or WiMax). Taking into consideration also that in most urban settings, fully installed and operational communication infrastructure supporting known communication standards is virtually everywhere. If the earlier mentioned considerations are viable, then the WSN can be remotely reconfigured to take advantage of the available infrastructure (gateways and base stations) already on the ground instead of setting up new ones.   Adopting the intended model reduces the cost of deploying and installing new gateways and possibly new base stations.

The nodes can easily adopt future communication standards whenever they become available. Instead of retrieving older nodes and replacing them with newer ones, the older ones can simply be reconfigured on the fly thereby enabling them to function within evolving context requirements.

Capturing  and  using  context  information  during  reconfiguration  processes  can  be helpful   in   intelligently   managing   the   node’s   resources.   This   guaranty   optimal performance and efficient use of scarce available resources (energy and memory space) In view of these needs and observed deficiencies in existing approaches, the research work is intended to address the following:

      Reduce the presence of redundant codes, thereby lessening the size of the firmware deployed to the wireless sensor nodes;

      Enhance   the   flexibility  of   reuse;   allow   real-time   user   input   during reconfiguration processes and autonomous reconfiguration using fuzzy logic in decision making;

      Establish a two-way interactive platform between the reconfiguring agent (user via base station) and the reconfigured (sensor node) and by extension the entire wireless sensor network. The two-way interactive platform enables the base station to assess the state of the sensor node through the contextual information it relayed. In addition, coupled with other relevant information (operational related contextual information), the system then decides when and how or what manner of reconfiguration should be employed. The aim is to ensure that the entire network performs efficiently and optimally manages the available resources (memory usage and energy consumption)

      Include  artificial  intelligence  techniques  (fuzzy  logic)  in  reconfiguration processes to enable the entire system autonomously respond to evolving changes especially in unfriendly environments.

The benefits of the model are to reduce energy consumption rate and effect a reduction in the amount of memory-space  used while reprogramming a wireless sensor node. The inclusion of artificial intelligence techniques (fuzzy logic) in reconfiguration processes enables the entire system to  respond to evolving changes in an unfriendly environment.

1.2  Problem Statement

Steine,  Ngo,  Oliver,  Geilen,  Basten,  Fohler  and  Decotgnie  (2011)  introduced  an approach that exploits design-time knowledge of the application scenario dynamics to construct and implements a proactive runtime reconfiguration paradigm. However, two challenging issues are apparent here: , the possibility of capturing all anticipated reconfiguration needs can be challenging, and the scarcely available memory space might not be sufficient to accommodate codes written to address these needs. Moreover, even if it does, there is the likelihood of redundant codes written to handle anticipated changes, which might never occur, and invariably taking up scarcely available memory spaces. A review of existing reconfiguration approaches and related challenges (energy consumption rate and memory space ) is reported in Eronu, Misra and Aibinu (2013).

In addition, implementing WSN reconfiguration may depend on whether it is needful, urgent,  or  sustainable.  For  example,  instead  of  effecting  reconfiguration  procedure during unfavourable weather conditions, it may be needful to delay the process and then resume when the conditions become favourable. In extreme cases, it is advisable to stop the process completely when the available energy in the node cannot sufficiently sustain the reconfiguration process. Where the second option is the norm, the sensor node might not  be  able  to  implement  new  functionalities  but  it  can  still  be  utilised  for  other purposes not dependent on the update. The ability to take decisions of this nature is largely confined to the human domain. However, Artificial Intelligence (AI) techniques like the Fuzzy Logic and Artificial Neural Network allow machines to mimic human cognitive capabilities. Importantly, the problem needs to be presented as defined input variables and the output variables make-up the solutions. Solutions are obtained from the analyses of processed input variables in conformity with a set of rules that are based or derived from expert knowledge.

1.3 Aim and Objectives

The aim of this research is to develop a software system that dynamically reconfigures wireless sensor network operational functionalities optimally based on evolving application context. In order to realise the aforementioned aim, the under listed set of objectives were actualised and used to devise result-oriented procedures:

I.            To devise a WSN context based reconfiguration model;

II.            To  design  and  implement  a  metric  utility  for  measuring  the  degree  of changes made in modified application source codes and relaying the exact changes;

III.           To integrate and use fuzzy logic controller in deciding the most appropriate reconfiguration approach to adopt in response to evolving application or operational context; and

IV.         To evaluate the performance of the developed system.

1.4 Limitation of Study

The Execution  Link  File (ELF) format adopted for developing the Precision Delta Extraction (PDE) tool in this work is not implemented in certain operating systems like the TinyOS. Hence, this limitation has constrained most of the work to only sensors nodes that employ the ELF format in their firmware generation and deployment.

1.5   Scope of Study

Several reconfiguration approaches are currently being implemented at various layers of the sensor node architecture. Majority of these approaches are still under development; that is, research are still on and their possible adoption in real life application scenario appears remote. For example, the use of field programmable gate arrays (FPGA) to actualise reconfigurable processors or rather soft-processors for wireless sensor nodes is not feasible now. More detailed information on the implementation of selected reconfiguration approaches at four layers is presented in Chapter Two. However, in this research work, the design and implementation processes are confined to the operating system platform.

1.6       Thesis Outline

The general introduction, statement of the problem, the aim, objectives and justification of the work were presented in this Chapter.  Chapter Two presents a review of several wireless sensor reconfiguration research works from the following perspective: the driving factors necessitating reconfiguration needs, previous and current reconfiguration approaches at some selected layers of the sensor node. The four selected layers are namely: the application, middleware, processing elements and the operating system layers. In addition, challenges and lapses associated with these approaches as implemented in the various layers were also presented. Also, further discussion on how these lapses can be addressed using surrounding contextual information presented. In Chapter Three, a detailed description of the research methodology presented. The description spans over the design and development of the context based reconfiguration software system for wireless sensor network model. The formulation and application of two additional subcomponents namely the precise delta extraction tool and a fuzzy logic controller were discussed. In addition, the testbed composition and setup for evaluating the model’s pilot data, and the simulation tool employed to evaluate the model on a larger scale  are  presented. Chapter Four presents  the results  and  discussion  of the research. Finally,    Chapter    Five    presents    some    concluding    remarks    and recommendations for future works.

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