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Introduction

The processing of a crime scene is among the most critical characteristics of active and successful criminal investigations (Liu et al., 2018). The outcome of forensic science includes accurate crime scene investigations that focus on identifying, collecting, packaging properly, transporting and analysing evidence materials. Doyle (2010) argued that the different unconfirmed pieces of evidence discovered in the crime scene could turn out to be compelling forensic pieces of evidence if they are searched schematically and dealt with appropriately. A professional crime scene investigator differs from a local police officer, in that, the professional crime scene investigator knows that a crime scene is sensitive, and as such, meticulously puts in energy to identify and gather forensic evidence from the crime scene (Conway et al., 2015). Edwards (2005) stated that the evidence could be lost, or its quality may be inadequate if crime scenes are not appropriately handled. It could lead to wrongful convictions or exonerations in a criminal justice system.

Julian et al., (2012) noted that the courts of nowadays rely on the forensic analysis of pieces of evidence obtained from the crime scene, to a great extent. In so doing, they adopt a more objective approach for a final verdict. The appreciation of the relevance of crime scene has resulted in the analysis of its role in the current process of making policy, and model building to minimise crime risks, and to also prevent the crime from occurring at all (Yaqoob et al., 2019). Policing based on intelligence also requires documenting evidence and the purpose of obtaining such evidence. It makes it necessary to involve the services of a professional and experienced crime scene investigator to process the crime scene (van den Eeden et al., 2016). When a crime scene is properly analysed, it gives rise to an intelligence-led policing because forensics intelligence could be developed from the extracted information from the crime scene analysis. Additionally, Ribaux et al. (2010) believe that the pieces of evidence obtained from crime scenes can help use these bits of intelligence to build future security models.

Some examples from the past show that the court proceedings could avoid convicting wrongfully if crime scene investigations are correctly handled (Sorocham, 2008). The criminal present at the crime scene can be identified and individualised as a result of the evidence obtained from the crime scene (Kaye, 2009). Therefore, the correct approaches and processes for the obtaining, management, storage, and subsequent delivery of evidence to the forensic laboratory to downstream test the evidence material must be applied (Sollie et al., 2017). Usually, the evidence may not be recognised because of the lack of suitable techniques and training, and it may also not be obtained at all. In cases whereby such pieces of evidence are collected, they may not be preserved. They could also be compromised, even when the correct process is applied; if compromised, the laboratory process may become futile (Peterson et al., 2013)

Problem Statement

Digital forensic (DF) emerged as a result of the increase in the use of electronic devices in criminal activities. Its focus is on obtaining, investigating and presenting evidence in a way that is acceptable in a court (Rajendran and Gopalan, 2016). Several studies (Peterson et al., 2013; de Gruijter et al., 2016; de Gruijter and de Poot, 2019) show that processing the crime scene and collecting most appropriate evidence and information is vital for crime investigation. Therefore, it is expected that forensic investigators should be well trained in order to identify the evidence at the crime scene. However, it is identified that the officials who handle the crime scene to gather evidence are not professionally trained with visual aids. Therefore, an inexperienced official will have challenges in identifying the vital evidence in the crime scene. Consequently, the inexperienced officials are required to get visual training on finding substantial evidence in the crime scene. The interest in online games has increased recently. More people across various age range have developed an interest in it. (Kwon et al., 2016). Therefore, this project intends to develop the application in an interactive manner that will entertain the users and will encourage them to complete the training.

Aim and Objectives

This project aims at developing a mobile application that will allow the inexperienced forensic officials to train themselves to identify the vital evidence in the crime scene. The objectives identified for this project are discussed below:

• Objective 1: To perform secondary research to identify the factors that need to be considered when identifying the vital evidence in the crime scene.
• Objective 2: To identify similar applications that are currently available for training inexperienced forensic officials.
• Objective 3: To perform primary research to identify the required functionality suggested by existing forensic officials.
• Objective 4: To implement the mobile application and perform various testing to ensure that the developed application is error-free.
• Objective 5: To examine the strengths and weaknesses of the developed application.

Research Questions

The identified research questions for this project are provided below:

• What are the factors that need to be considered when determining relevant evidence in a crime scene?
• What are the applications currently available to train the inexperienced officials to identify the evidence in a crime scene?

Deliverables

The deliverables of this project is a mobile application and a report. The mobile app should be able to assist the inexperienced forensic officials in training themselves to identify the evidence in the crime scene. Also, the report will consist of the complete documentation of the application developed, full test cases and the results, the secondary research conducted and the functional requirements of the application identified from the primary research.  

Relevance

This project mainly focuses on security and forensic computing as it is introducing a mobile application that can be used by the inexperienced forensic investigators to train themselves to gather relevant evidence from the crime scene.

Methodology

This project focuses on secondary research, primary research and software development, and they are discussed below:

Secondary research

The secondary research in this project will utilise a systematic approach (Johnson et al., 2016) to review the works of literature. The steps involved in the systematic review of the literature are provided below:

• Step 1: Identify the research questions that can be used for the project.
• Step 2: Identify the keywords that should be used to research the works of literature.
• Step 3: Extract the journals and books that are appropriate for this project.
• Step 4: Write the literature review chapter.

Primary Research

The primary research will focus on a mixed methodology (Mackey, and Bryfonski, 2018). The mixed-method will include quantitative and qualitative analysis (Abro et al., 2015). The quantitative analysis will mainly focus on gathering information that is quantifiable using an online survey. On the other hand, qualitative research will primarily focus on collecting descriptive information using interview (McCusker, and Gunaydin, 2015). The responses received from the survey and interview will be used to derive the functional requirements of the mobile application.

Software Development

The Spiral model will be used for mobile application development in this project. Even though there are several works of research suggesting the use of the waterfall model because it is the oldest model, this project will not be using it because the stages are very static and once a phase is completed it cannot be revisited (Alshamrani, A. and Bahattab, 2015). The static property of the waterfall model is not suitable for this project as the project features will change with time. However, the agile method is widely used in the industry,  but, it will not be used in this project because the agile is more suitable for team project which provides a clear description of roles and responsibilities of the team members (Krishnan, 2015). The Software Development Life Cycle (SDLC) will be used for each phase of the spiral model (Ali, 2017). The complete discussion of the stages of the SDLC are provided below:

• Requirement Analysis (Stage 1): This is the initial stage where the functional, non-functional and security Requirements of the mobile application will be identified using the primary research.
• Design (Stage 2): In this stage will include drawing UML diagrams such as a class diagram, Entity Relationship diagram, activity diagram, use case diagram and activity diagram to study the functional flow of the application
• Implementation (Stage 3): The development of the application will be done in Java on the Android studio.
• Testing (Stage 4): Several testing will be conducted to ensure that the application developed is error-free. Some of the testings that will be used in this project are unit testing, integration testing, acceptance testing, and security testing.
• Documentation (Stage 5): The developed code will be commented as well as the Read Me file will be written in order to assist the user in running the application.

Evaluation

The risk assessment conducted for this project is provided in the table below:

Table 1: Risk assessment

Risk	Impact	Mitigation Plan
Not able to meet the deadline	Low	Get an extension from the supervisor on time.
Data Loss	Medium	Regularly backup the mobile development Java code and the report to avoid data loss.
Not have sufficient knowledge to do the mobile development	High	Refer to online forums and tutorials to study Android programming in Java.

 

Schedule

Table 2: Project Plan

Task Name	Start Date	End Date	Duration (Days)
Initial Research	23/09/2019	07/10/2019	14
Proposal	07/10/2019	28/10/2019	21
Secondary research	28/10/2019	28/11/2019	31
Primary Research	13/11/2019	23/11/2019	10
Introduction Chapter	23/11/2019	28/11/2019	5
Methodology Chapter	28/11/2019	03/12/2019	5
Literature Review Chapter	28/11/2019	18/12/2019	20
Requirement Analysis Chapter	03/12/2019	08/12/2019	5
Design Chapter	03/12/2019	08/12/2019	5
Development of the application	08/12/2019	11/02/2020	65
Testing of the application	13/12/2019	26/02/2020	75
Presentation 1	08/12/2019	16/12/2019	8
Implementation Chapter	26/02/2020	07/03/2020	10
Testing Chapter	07/03/2020	17/03/2020	10
Evaluation Chapter	17/03/2020	22/03/2020	5
Conclusion  Chapter	22/03/2020	24/03/2020	2
Project Management Chapter	24/03/2020	26/03/2020	2
Abstract and compiling the report	26/03/2020	27/03/2020	1
Proofread the report	27/03/2020	06/04/2020	10
Presentation 2	06/04/2020	16/04/2020	10

Figure 1: Gantt Chart

References

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Ali, K., 2017. A Study of Software Development Life Cycle Process Models. International Journal of Advanced Research in Computer Science, 8(1).

Alshamrani, A. and Bahattab, A., 2015. A comparison between three SDLC models waterfall model, spiral model, and Incremental/Iterative model. International Journal of Computer Science Issues (IJCSI), 12(1), p.106.

Conway, A., James, J.I. and Gladyshev, P., 2015, October. Development and initial user evaluation of a virtual crime scene simulator including digital evidence. In International Conference on Digital Forensics and Cyber Crime (pp. 16-26). Springer, Cham.

de Gruijter, M. and de Poot, C.J., 2019. The use of rapid identification information at the crime scene; similarities and differences between English and Dutch CSIs. Policing and Society, 29(7), pp.848-868.

de Gruijter, M., de Poot, C.J. and Elffers, H., 2016. The influence of new technologies on the visual attention of CSIs performing a crime scene investigation. Journal of forensic sciences, 61(1), pp.43-51.

Doyle, A.C., 2010. Methodical Approach to Processing the Crime Scene. An Introduction to Crime Scene Investigation, pp.103-133.

Edwards, K., 2005. Ten things about DNA contamination that lawyers should know. Criminal Law Journal, 29(2), pp.71-93.

Johnson, D., Deterding, S., Kuhn, K.A., Staneva, A., Stoyanov, S. and Hides, L., 2016. Gamification for health and wellbeing: A systematic review of the literature. Internet interventions, 6, pp.89-106.

Julian, R., Kelty, S. and Robertson, J., 2012. “Get it right the first time”: Critical Issues at the Crime Scene. Current issues in criminal justice, 24(1), pp.25-37.

Kaye, D.H., 2009. Probability, Individualisation, and Uniqueness in Forensic Science Evidence-Listening to the Academies. Brook. L. Rev., 75, p.1163.

Krishnan, M.S., 2015. Software development risk aspects and success frequency on spiral and agile model. International Journal of Innovative research in computer and communication Engineering, (3), p.1.

Kwon, H., Mohaisen, A., Woo, J., Kim, Y., Lee, E. and Kim, H.K., 2016. Crime scene reconstruction: Online gold farming network analysis. IEEE Transactions on Information Forensics and Security, 12(3), pp.544-556.

Liu, Y., Peng, Y., Li, D., Fan, J. and Li, Y., 2018, March. Crime Scene Investigation Image Retrieval with Fusion CNN Features Based on Transfer Learning. In Proceedings of the 3rd International Conference on Multimedia and Image Processing (pp. 68-72). ACM.

Mackey, A. and Bryfonski, L., 2018. Mixed methodology. In The Palgrave Handbook of Applied Linguistics Research Methodology (pp. 103-121). Palgrave Macmillan, London.

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Peterson, J.L., Hickman, M.J., Strom, K.J. and Johnson, D.J., 2013. Effect of forensic evidence on criminal justice case processing. Journal of forensic sciences, 58, pp.S78-S90.

Rajendran, S. and Gopalan, N.P., 2016. Mobile Forensic Investigation (MFI) life cycle process for digital data discovery (DDD). In Proceedings of the International Conference on Soft Computing Systems (pp. 393-403). Springer, New Delhi.

Ribaux, O., Baylon, A., Lock, E., Delemont, O., Roux, C., Zingg, C. and Margot, P., 2010. Intelligence-led crime scene processing. Part II: Intelligence and crime scene examination. Forensic science international, 199(1-3), pp.63-71.

Sollie, H., Kop, N. and Euwema, M.C., 2017. Mental resilience of crime scene investigators: How police officers perceive and cope with the impact of demanding work situations. Criminal justice and behavior, 44(12), pp.1580-1603.

Sorocham, D.J., 2008. Wrongful convictions: preventing miscarriages of justice some case studies. Tex. Tech L. Rev., 41, p.93.

van den Eeden, C.A., de Poot, C.J. and Van Koppen, P.J., 2016. Forensic expectations: Investigating a crime scene with prior information. Science & justice, 56(6), pp.475-481.

Yaqoob, I., Hashem, I.A.T., Ahmed, A., Kazmi, S.A. and Hong, C.S., 2019. Internet of things forensics: Recent advances, taxonomy, requirements, and open challenges. Future Generation Computer Systems, 92, pp.265-275.

Last updated: Mar 23, 2020 05:38 PM

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